{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import hashlib\n",
    "\n",
    "os.environ[\"CUDA_LAUNCH_BLOCKING\"] = \"1\"\n",
    "os.makedirs(\"./out\", exist_ok=True)\n",
    "sys.path.append('../../pybind/build')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class KVCache:\n",
    "    pass\n",
    "\n",
    "class KVCacheNone(KVCache):\n",
    "    def __init__(self):\n",
    "        self.cache = {}\n",
    "    \n",
    "    def put(self, layer, idx, key, value):\n",
    "        self.cache[(layer, idx)] = (key, value)\n",
    "\n",
    "    def get(self, layer, idx):\n",
    "        return self.cache.get((layer, idx), None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class WeightWrapper:\n",
    "    name = None\n",
    "    full_name = None\n",
    "    weight_map = None\n",
    "    shape = None\n",
    "\n",
    "def process_weight_none(global_weight_map, weight_name, weight_wrapper, total_layer, cached = False):\n",
    "    return None\n",
    "\n",
    "def process_weight_no_transpose(global_weight_map, weight_name, weight_wrapper, total_layer, cached = False):\n",
    "    weight_wrapper.weight_map = {}\n",
    "    for l in range(total_layer):\n",
    "        weight_wrapper.weight_map[l] = global_weight_map[weight_name.format(layer = l)]\n",
    "        assert weight_wrapper.weight_map[l].shape == weight_wrapper.shape, f\"name = {weight_name}, shape = {weight_wrapper.shape}, layer = {l}, shape = {weight_wrapper.weight_map[l].shape}\"\n",
    "    return weight_wrapper\n",
    "\n",
    "def process_weight_layer(global_weight_map, weight_name, weight_wrapper, total_layer, cached = False):\n",
    "    weight_wrapper.weight_map = {}\n",
    "    for l in range(total_layer):\n",
    "        weight_wrapper.weight_map[l] = global_weight_map[weight_name.format(layer = l)].t()\n",
    "        assert weight_wrapper.weight_map[l].shape == weight_wrapper.shape, f\"name = {weight_name}, shape = {weight_wrapper.shape}, layer = {l}, shape = {weight_wrapper.weight_map[l].shape}\"\n",
    "    return weight_wrapper\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from enum import Enum\n",
    "import numpy as np\n",
    "import torch\n",
    "import bind_genEmbedding\n",
    "\n",
    "class IOBufferType(Enum):\n",
    "    FULL = 1\n",
    "    DiscontinousPartition = 2\n",
    "    ContinousPartition = 3\n",
    "    PartialSum = 4\n",
    "\n",
    "class IOBufferTransform(Enum):\n",
    "    Replicate = 1\n",
    "    Partition = 2\n",
    "    Aggregate = 3\n",
    "\n",
    "class IOWrapper:\n",
    "    def __init__(self, owner, name, IOtype, dtype=torch.float16):\n",
    "        self.owner = owner  # owner is now an Operations object or similar\n",
    "        self.name = name\n",
    "        self.prev = []\n",
    "        self.next = []\n",
    "        self.prev_depend_on_prev_layer = []\n",
    "        self.shape = None # shape [0] is non-contiguous dimension, shape [1] is contiguous dimension\n",
    "        self.ptr = 0\n",
    "        self.IOtype = IOtype\n",
    "        self.tensor: torch.Tensor = None\n",
    "        self.child = []\n",
    "        self.transform = None\n",
    "        self.tensor_offset = 0\n",
    "        self.dtype = dtype\n",
    "        \n",
    "    \n",
    "    @property\n",
    "    def tensor_range(self):\n",
    "        if self.shape is None:\n",
    "            return None\n",
    "        return (self.tensor_offset, self.tensor_offset + self.shape[0])\n",
    "    \n",
    "    @property\n",
    "    def fullName(self):\n",
    "        owner_name = self.owner.name if hasattr(self.owner, \"name\") else str(self.owner)\n",
    "        return f\"{owner_name}_{self.name}\"\n",
    "    \n",
    "    def chain(self, next_wrapper, depend_on_prev=False):\n",
    "        self.next.append(next_wrapper)\n",
    "        next_wrapper.prev.append(self)\n",
    "        next_wrapper.prev_depend_on_prev_layer.append(depend_on_prev)\n",
    "        # check dtype must be the same\n",
    "        if self.dtype != next_wrapper.dtype:\n",
    "            raise Exception(f\"Error: {self.fullName} and {next_wrapper.fullName} has different dtype\")\n",
    "        \n",
    "        return next_wrapper\n",
    "    \n",
    "    def __rshift__(self, next_wrapper):\n",
    "        return self.chain(next_wrapper)\n",
    "    \n",
    "    def toStr(self):\n",
    "        # name, prev = [], next = []\n",
    "        return f\"{self.fullName}, prev = {[p.fullName for p in self.prev]}, next = {[n.fullName for n in self.next]}\"\n",
    "    \n",
    "    def checkCorrectPartition(self):\n",
    "        # actually only three case: same io, aggregate, partition. Cannot discontious partition\n",
    "\n",
    "        # case 1: No next wrapper\n",
    "        if len(self.next) == 0:\n",
    "            return True\n",
    "        \n",
    "        # pre-check: all next types are the same (differ from self is OK)\n",
    "        if not (all(n.IOtype == self.next[0].IOtype for n in self.next)):\n",
    "            print(f\"Error: {self.fullName} has different IOtype with its next\")\n",
    "            for wrapper in self.next:\n",
    "                print(f\"  {wrapper.fullName}, IOtype = {wrapper.IOtype}\")\n",
    "            return False\n",
    "        \n",
    "        next_type = self.next[0].IOtype\n",
    "        \n",
    "        # case 2: same IO type, same size\n",
    "        if next_type == self.IOtype:\n",
    "            if all(n.shape == self.shape for n in self.next):\n",
    "                self.transform = IOBufferTransform.Replicate\n",
    "                return True\n",
    "            else:\n",
    "                print(f\"[Replicate Failure] {self.fullName}: Expected shape {self.shape}\")\n",
    "                for n in self.next:\n",
    "                    print(f\"  {n.fullName}, shape = {n.shape}\")\n",
    "                return False\n",
    "\n",
    "        # case 3: Partition\n",
    "        if self.IOtype == IOBufferType.FULL and next_type == IOBufferType.ContinousPartition:\n",
    "            total_rows = sum(n.shape[0] for n in self.next)\n",
    "            if total_rows == self.shape[0]:\n",
    "                self.child = self.next\n",
    "                self.transform = IOBufferTransform.Partition\n",
    "                return True\n",
    "            else:\n",
    "                print(f\"[ContinousPartition Failure] {self.fullName}: Sum of partition rows is {total_rows} but expected {self.shape[0]}.\")\n",
    "                for n in self.next:\n",
    "                    print(f\"  {n.fullName}, shape = {n.shape}\")\n",
    "                return False\n",
    "            \n",
    "            \n",
    "        # case 3: aggregate\n",
    "        if self.IOtype == IOBufferType.ContinousPartition and next_type == IOBufferType.FULL:\n",
    "            # consider all next wrappers' previous wrappers\n",
    "            for n in self.next:\n",
    "                for p in n.prev:\n",
    "                    if p.IOtype != IOBufferType.ContinousPartition:\n",
    "                        print(f\"[ContinousAggregation Failure] {self.fullName}: Expected all next wrappers' previous wrappers to be ContinousPartition.\")\n",
    "                        return False\n",
    "                if sum(p.shape[0] for p in n.prev) != n.shape[0]:\n",
    "                    print(f\"[ContinousAggregation Failure] {self.fullName}: Sum of partition rows is {sum(p.shape[0] for p in n.prev)} but expected {n.shape[0]}.\")\n",
    "                    for p in n.prev:\n",
    "                        print(f\"  {p.fullName}, shape = {p.shape}\")\n",
    "                    return False\n",
    "                n.child = n.prev\n",
    "            self.transform = IOBufferTransform.Aggregate\n",
    "            return True\n",
    "        \n",
    "        # Fallback: configuration does not match any recognized partition scheme.\n",
    "        next_types = [n.IOtype for n in self.next]\n",
    "        next_shapes = [n.shape for n in self.next]\n",
    "        print(f\"[Partition Check Failure] {self.fullName}: Unrecognized partition configuration.\")\n",
    "        print(f\"Self IOtype: {self.IOtype}, shape: {self.shape}\")\n",
    "        print(f\"Next partitions IOtypes: {next_types}\")\n",
    "        print(f\"Next partitions shapes: {next_shapes}\")\n",
    "        return False\n",
    "\n",
    "class Operations:\n",
    "    def __init__(self, name):\n",
    "        self.inputs = {}\n",
    "        self.outputs = {}\n",
    "        self.weights = {}\n",
    "        self.externals = {}\n",
    "        self.name = name\n",
    "        self.first_layer_only = False\n",
    "        self.last_layer_only = False\n",
    "        self.weight_name = None\n",
    "    \n",
    "    @property\n",
    "    def prerequisites(self):\n",
    "        dep = []\n",
    "        for _, input_wrapper in self.inputs.items():\n",
    "            for dep_wrapper, prev_layer in zip(input_wrapper.prev, input_wrapper.prev_depend_on_prev_layer):\n",
    "                dep.append((dep_wrapper.owner, prev_layer))\n",
    "        \n",
    "        return dep\n",
    "        \n",
    "    def checkConnection(self):\n",
    "        for name, IOwrapper in self.inputs.items():\n",
    "            if IOwrapper.prev is None:\n",
    "                raise Exception(f\"Operation {self.name}, Input {name} is not connected\")\n",
    "    \n",
    "    def setWeightName(self, name):\n",
    "        self.weight_name = name\n",
    "        return self\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached = False):\n",
    "        return process_weight_none(global_weight_map, self.weight_name, None, total_layers, cached)\n",
    "    \n",
    "    def first_only(self):\n",
    "        self.first_layer_only = True\n",
    "        return self\n",
    "    \n",
    "    def last_only(self):\n",
    "        self.last_layer_only = True\n",
    "        return self\n",
    "    \n",
    "    def __str__(self):\n",
    "        return self.name    \n",
    "\n",
    "# -------------------------------------------------------------------\n",
    "# Operations definitions (ordered loosely by type)\n",
    "\n",
    "class GlobalInput(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {}\n",
    "        self.outputs = {\n",
    "            \"tokens\": IOWrapper(self, 'tokens', IOBufferType.FULL, dtype=torch.int32)\n",
    "        }\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.outputs[\"tokens\"].shape = (self.batch_size,)\n",
    "    \n",
    "    def run(self, layer):\n",
    "        pass\n",
    "\n",
    "class GenEmbedding(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"token\": IOWrapper(self, 'token', IOBufferType.FULL, dtype=torch.int32),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.FULL)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"embedding\": WeightWrapper()\n",
    "        }\n",
    "    \n",
    "    def setShape(self, hidden_dim, vocab_size):\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.vocab_size = vocab_size\n",
    "        self.weights[\"embedding\"].shape = (vocab_size, hidden_dim)\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"token\"].shape = (self.batch_size,)\n",
    "        self.outputs[\"output\"].shape = (self.batch_size, self.hidden_dim)\n",
    "        \n",
    "    def run(self, layer):\n",
    "        # Retrieve token indices from input and the embedding matrix from weight_map.\n",
    "        tokens = self.inputs[\"token\"].tensor  # expected shape: (1, batch_size)\n",
    "        print(self.inputs[\"token\"].tensor.device)\n",
    "        embedding = self.weights[\"embedding\"].weight_map[layer]  # expected shape: (vocab_size, hidden_dim)\n",
    "        print(embedding[tokens])\n",
    "        # create a new tensor to store the output\n",
    "        output_temp = torch.zeros((self.batch_size, self.hidden_dim), dtype=torch.float16, device=self.inputs[\"token\"].tensor.device)\n",
    "        bind_genEmbedding.genEmbedding(tokens, embedding, output_temp)\n",
    "\n",
    "        # self.outputs[\"output\"].tensor.copy_(embedding[tokens])\n",
    "        self.outputs[\"output\"].tensor.copy_(output_temp)\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached = False):\n",
    "        return process_weight_no_transpose(global_weight_map, self.weight_name, self.weights[\"embedding\"], total_layers, cached)\n",
    "    \n",
    "class LayerNorm(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"input\": IOWrapper(self, 'input', IOBufferType.FULL),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.FULL)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"weight\": WeightWrapper(),\n",
    "        }\n",
    "    \n",
    "    def setShape(self, hidden_dim):\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.weights[\"weight\"].shape = (self.hidden_dim,)\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"input\"].shape = (self.batch_size, self.hidden_dim)\n",
    "        self.outputs[\"output\"].shape = (self.batch_size, self.hidden_dim)\n",
    "        \n",
    "    def run(self, layer):\n",
    "        x = self.inputs[\"input\"].tensor\n",
    "        x = x.to(torch.float32)\n",
    "        epsilon = 1e-5\n",
    "        # Compute the root-mean-square (RMS) along the last dimension.\n",
    "        rms = torch.sqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + epsilon)\n",
    "        normalized_x = x / rms\n",
    "        weight_val = self.weights[\"weight\"].weight_map[layer]\n",
    "        # Scale the normalized values by the learned weight.\n",
    "        self.outputs[\"output\"].tensor.copy_(normalized_x.to(torch.float16) * weight_val)\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached = False):\n",
    "        return process_weight_layer(global_weight_map, self.weight_name, self.weights[\"weight\"], total_layers, cached)\n",
    "        \n",
    "\n",
    "class GEMMNoBias(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"A\": IOWrapper(self, 'A', IOBufferType.FULL),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"D\": IOWrapper(self, 'D', IOBufferType.FULL)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"B\": WeightWrapper()\n",
    "        }\n",
    "    \n",
    "    def setShape(self, N, K):\n",
    "        self.N = N\n",
    "        self.K = K        \n",
    "        self.weights[\"B\"].shape = (self.K, self.N)\n",
    "    \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"A\"].shape = (self.M, self.K)\n",
    "        self.outputs[\"D\"].shape = (self.M, self.N)\n",
    "\n",
    "        \n",
    "    def run(self, layer):\n",
    "        A = self.inputs[\"A\"].tensor\n",
    "        B = self.weights[\"B\"].weight_map[layer]\n",
    "        self.outputs[\"D\"].tensor.copy_(A.matmul(B))\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached = False):\n",
    "        return process_weight_layer(global_weight_map, self.weight_name, self.weights[\"B\"], total_layers, cached)\n",
    "\n",
    "class GEMMCombineWeight(GEMMNoBias):\n",
    "    def setWeightName(self, name_list):\n",
    "        self.weight_name = name_list\n",
    "        return self\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached=False):\n",
    "        self.weights[\"B\"].weight_map = {}\n",
    "        for l in range(total_layers):\n",
    "            self.weights[\"B\"].weight_map[l] = torch.cat([global_weight_map[name.format(layer=l)].t() for name in self.weight_name], dim=1).contiguous()\n",
    "\n",
    "import math\n",
    "import torch\n",
    "\n",
    "# (Assume that the following classes and enums are defined somewhere in your framework.)\n",
    "# from your_framework import Operations, IOWrapper, IOBufferType\n",
    "\n",
    "class RopeAppend(Operations):\n",
    "    def __init__(\n",
    "        self,\n",
    "        name,\n",
    "        rope_type=\"llama3\",\n",
    "        theta=10000.0,\n",
    "        factor=8.0,\n",
    "        low_freq_factor=1.0,\n",
    "        high_freq_factor=4.0,\n",
    "        original_max_position_embeddings=8192,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            name (str): The name of this operator.\n",
    "            rope_type (str): The type of RoPE implementation to use. For llama3, pass \"llama3\".\n",
    "            theta (float): The base used to compute the inverse frequency (typically set from config.rope_theta).\n",
    "            factor (float): Scaling factor used in llama3.\n",
    "            low_freq_factor (float): Lower bound frequency factor (llama3).\n",
    "            high_freq_factor (float): Upper bound frequency factor (llama3).\n",
    "            original_max_position_embeddings (int): The original maximum context length used in pretraining.\n",
    "        \"\"\"\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\"kqv\": IOWrapper(self, \"kqv\", IOBufferType.FULL)}\n",
    "        self.outputs = {\"q\": IOWrapper(self, \"q\", IOBufferType.FULL)}\n",
    "        self.externals = {\"KVCache\": None}\n",
    "        \n",
    "        # Save RoPE configuration.\n",
    "        self.rope_type = rope_type\n",
    "        self.theta = theta  # typically config.rope_theta\n",
    "        self.factor = factor\n",
    "        self.low_freq_factor = low_freq_factor\n",
    "        self.high_freq_factor = high_freq_factor\n",
    "        self.original_max_position_embeddings = original_max_position_embeddings\n",
    "\n",
    "    def setShape(self, num_kv_heads, num_qo_heads, head_dim):\n",
    "        self.num_kv_heads = num_kv_heads\n",
    "        self.num_qo_heads = num_qo_heads\n",
    "        self.head_dim = head_dim\n",
    "\n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        # The input tensor \"kqv\" is assumed to have a flattened layout:\n",
    "        # [batch_size, (num_qo_heads + 2 * num_kv_heads) * head_dim]\n",
    "        self.inputs[\"kqv\"].shape = (\n",
    "            self.batch_size,\n",
    "            (self.num_qo_heads + 2 * self.num_kv_heads) * self.head_dim,\n",
    "        )\n",
    "        # The output \"q\" has shape [batch_size, num_qo_heads * head_dim]\n",
    "        self.outputs[\"q\"].shape = (self.batch_size, self.num_qo_heads * self.head_dim)\n",
    "\n",
    "    def update(self, qo_indicies):\n",
    "        \"\"\"Stores the starting indices for the query/key segments.\"\"\"\n",
    "        self.qo_indicies = qo_indicies\n",
    "\n",
    "    def rotate_half(self, x):\n",
    "        \"\"\"Rotates the last half of the last dimension.\"\"\"\n",
    "        dim = x.shape[-1]\n",
    "        x1 = x[..., : dim // 2]\n",
    "        x2 = x[..., dim // 2 :]\n",
    "        return torch.cat([-x2, x1], dim=-1)\n",
    "\n",
    "    def apply_rope(self, x, offset=0):\n",
    "        \"\"\"\n",
    "        Applies RoPE to the tensor `x` (of shape [seq_len, head_dim]). For llama3,\n",
    "        we adjust the inverse frequency vector as described in the paper.\n",
    "        \n",
    "        Args:\n",
    "            x (torch.Tensor): Input tensor with shape [seq_len, head_dim].\n",
    "            offset (int): The starting position offset.\n",
    "        \n",
    "        Returns:\n",
    "            torch.Tensor: The rotated tensor.\n",
    "        \"\"\"\n",
    "        seq_len, dim = x.shape\n",
    "        device = x.device\n",
    "        dtype = x.dtype\n",
    "        positions = torch.arange(offset, offset + seq_len, device=device, dtype=dtype)\n",
    "\n",
    "        if self.rope_type == \"llama3.1\":\n",
    "            # Compute the basic inverse frequency vector using theta.\n",
    "            inv_freq = 1.0 / (\n",
    "                self.theta ** (torch.arange(0, dim, 2, device=device, dtype=dtype) / dim)\n",
    "            )\n",
    "            # Compute the wavelengths.\n",
    "            wavelen = 2 * math.pi / inv_freq\n",
    "            low_freq_wavelen = self.original_max_position_embeddings / self.low_freq_factor\n",
    "            high_freq_wavelen = self.original_max_position_embeddings / self.high_freq_factor\n",
    "\n",
    "            # For frequencies with wavelengths greater than the low bound, divide inv_freq by factor.\n",
    "            inv_freq_llama = torch.where(wavelen > low_freq_wavelen, inv_freq / self.factor, inv_freq)\n",
    "\n",
    "            # For values in between, interpolate smoothly.\n",
    "            smooth_factor = (self.original_max_position_embeddings / wavelen - self.low_freq_factor) / (\n",
    "                self.high_freq_factor - self.low_freq_factor\n",
    "            )\n",
    "            smoothed_inv_freq = (1 - smooth_factor) * (inv_freq_llama / self.factor) + smooth_factor * inv_freq_llama\n",
    "\n",
    "            # Identify indices where wavelengths are in the medium range.\n",
    "            is_medium_freq = (wavelen >= high_freq_wavelen) & (wavelen <= low_freq_wavelen)\n",
    "            inv_freq_final = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)\n",
    "        elif self.rope_type == \"llama3\":\n",
    "            base = 500000.0\n",
    "            dim = 128\n",
    "            inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim))\n",
    "            # print(\"inv_freq:\", inv_freq)\n",
    "            inv_freq_expanded = inv_freq[None, :, None].float().expand(1, -1, 1)\n",
    "            position_ids_expanded = positions[None, None, :].float()\n",
    "            with torch.autocast(device_type=device.type, enabled=False):\n",
    "                freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)\n",
    "                emb = torch.cat((freqs, freqs), dim=-1)\n",
    "                cos = emb.cos()\n",
    "                sin = emb.sin()\n",
    "            cos = cos.unsqueeze(1)\n",
    "            sin = sin.unsqueeze(1)\n",
    "\n",
    "            x = x.reshape(1, 7, -1, 128).transpose(1, 2)\n",
    "            print(\"x shape:\", x.shape)\n",
    "            print(\"cos shape:\", cos.shape)\n",
    "            x = x * cos + self.rotate_half(x) * sin\n",
    "            return x.transpose(1, 2).reshape(positions.shape[0], -1).to(dtype=dtype)\n",
    "            \n",
    "        else:\n",
    "            # Default RoPE: simply use the base theta.\n",
    "            inv_freq_final = 1.0 / (\n",
    "                self.theta ** (torch.arange(0, dim, 2, device=device, dtype=dtype) / dim)\n",
    "            )\n",
    "\n",
    "        # Compute the sinusoidal inputs.\n",
    "        \n",
    "        sinusoid_inp = torch.einsum(\"i,j->ij\", positions, inv_freq_final)\n",
    "        sin = sinusoid_inp.sin()\n",
    "        cos = sinusoid_inp.cos()\n",
    "\n",
    "        # Expand sin and cos to match x's dimension.\n",
    "        sin = torch.repeat_interleave(sin, repeats=2, dim=-1)\n",
    "        cos = torch.repeat_interleave(cos, repeats=2, dim=-1)\n",
    "\n",
    "        # Apply the RoPE transformation.\n",
    "        return x * cos + self.rotate_half(x) * sin\n",
    "\n",
    "    def run(self, layer):\n",
    "        \"\"\"\n",
    "        The run method splits the input `kqv` tensor into key, query, and value tensors,\n",
    "        applies RoPE (using the llama3 variant if selected) to the query and key portions,\n",
    "        and writes the updated keys to an external KV cache. The output \"q\" is set as a copy of v.\n",
    "        \"\"\"\n",
    "        kqv = self.inputs[\"kqv\"].tensor\n",
    "        # Determine the number of elements for each slice.\n",
    "        layout_strides = [\n",
    "            self.num_qo_heads * self.head_dim,\n",
    "            self.num_kv_heads * self.head_dim,\n",
    "            self.num_kv_heads * self.head_dim,\n",
    "        ]\n",
    "        # Split kqv into key, query, and value (here assumed to be in the order: k, q, v).\n",
    "        q, k, v = torch.split(kqv, layout_strides, dim=1)\n",
    "        k = k.contiguous()\n",
    "        v = v.contiguous()\n",
    "        q = q.contiguous()\n",
    "        # print shapes of k, q, v\n",
    "        print(f\"Layer {layer}: k shape: {k.shape}, q shape: {q.shape}, v shape: {v.shape}\")\n",
    "        \n",
    "\n",
    "        # Process each batch element.\n",
    "        for i in range(len(self.qo_indicies) - 1):\n",
    "            start = self.qo_indicies[i]\n",
    "            end = self.qo_indicies[i + 1]\n",
    "            sub_q = q[start:end, :]\n",
    "            sub_k = k[start:end, :]\n",
    "\n",
    "            # Apply RoPE to both the query and key sub-tensors.\n",
    "            sub_q = self.apply_rope(sub_q, offset=0)\n",
    "            sub_k = self.apply_rope(sub_k, offset=0)\n",
    "\n",
    "            # Write the updated values back.\n",
    "            q[start:end, :] = sub_q\n",
    "            k[start:end, :] = sub_k\n",
    "\n",
    "            # Update the external KVCache with the new key and value.\n",
    "            self.externals[\"KVCache\"].put(layer, i, sub_k, v[start:end, :])\n",
    "\n",
    "        self.outputs[\"q\"].tensor.copy_(q)\n",
    "\n",
    "    \n",
    "class DecAttn(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"Q\": IOWrapper(self, 'Q', IOBufferType.ContinousPartition),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.ContinousPartition)\n",
    "        }\n",
    "        self.externals = {\n",
    "            \"KVCache\": None\n",
    "        }\n",
    "    \n",
    "    def setShape(self, num_kv_heads, num_qo_heads, head_dim):\n",
    "        self.num_kv_heads = num_kv_heads\n",
    "        self.num_qo_heads = num_qo_heads\n",
    "        self.head_dim = head_dim\n",
    "        self.q_dim = num_qo_heads * head_dim\n",
    "        \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"Q\"].shape = (self.batch_size, self.q_dim)\n",
    "        self.outputs[\"output\"].shape = (self.batch_size, self.q_dim)\n",
    "    \n",
    "    def update(self, qo_indicies):\n",
    "        self.qo_indicies = qo_indicies\n",
    "    \n",
    "    def run(self, layer):\n",
    "        # Q = self.inputs[\"Q\"].tensor\n",
    "        # KVdata = self.externals[\"KVdata\"].tensor\n",
    "        # # Q: (batch_size, q_dim), KVdata: (batch_size, 2 * num_kv_heads * head_dim)\n",
    "        # # output: (batch_size, q_dim)\n",
    "        # self.outputs[\"output\"].tensor = Q @ KVdata.T\n",
    "        pass\n",
    "    \n",
    "class PFAttn(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"Q\": IOWrapper(self, 'Q', IOBufferType.ContinousPartition),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.ContinousPartition)\n",
    "        }\n",
    "        # Note: for consistency with other operators (like RopeAppend), we expect the external KV cache to be\n",
    "        # available as \"KVCache\". If needed, you can change the key name.\n",
    "        self.externals = {\n",
    "            \"KVCache\": None\n",
    "        }\n",
    "    \n",
    "    def setShape(self, num_kv_heads, num_qo_heads, head_dim):\n",
    "        self.num_kv_heads = num_kv_heads\n",
    "        self.num_qo_heads = num_qo_heads\n",
    "        self.head_dim = head_dim\n",
    "        self.q_dim = num_qo_heads * head_dim\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"Q\"].shape = (self.batch_size, self.q_dim)\n",
    "        self.outputs[\"output\"].shape = (self.batch_size, self.q_dim)\n",
    "    \n",
    "    def update(self, qo_indicies):\n",
    "        \"\"\"Stores the query offset indices for each batch element.  \n",
    "        qo_indicies should be a list (or tensor) of length (batch_size + 1) such that for each batch index i,  \n",
    "        the query slice is Q[qo_indicies[i]:qo_indicies[i+1], :].\n",
    "        \"\"\"\n",
    "        self.qo_indicies = qo_indicies\n",
    "    \n",
    "    def run(self, layer):\n",
    "        \"\"\"\n",
    "        For each batch element, this method retrieves the query portion (Q) of shape\n",
    "        [n_q, num_qo_heads * head_dim] and then uses the external KV cache to get the stored keys and values.\n",
    "        \n",
    "        The attention operation is performed per head as follows:\n",
    "        1. Reshape the query tensor to [n_q, num_qo_heads, head_dim].\n",
    "        2. Retrieve the cached keys and values (sub_k and sub_v) and reshape them to\n",
    "            [n_k, num_kv_heads, head_dim] (where n_k is the number of cached keys).\n",
    "        3. Compute the group size as: group_size = num_qo_heads // num_kv_heads.\n",
    "            This tells you how many query heads correspond to each key/value head.\n",
    "        4. Expand (repeat) the key and value tensors along the head dimension so that each query head\n",
    "            has a corresponding key and value (i.e. each key/value head is repeated group_size times).\n",
    "        5. Compute attention scores for each head via dot-product scaling (using the factor 1/sqrt(head_dim)).\n",
    "        6. **Apply a causal mask over the scores** so that each query position can only attend to\n",
    "            allowed keys (i.e. those coming from the past or up to the current position among the new tokens).\n",
    "        7. Apply softmax over the key dimension to obtain attention weights.\n",
    "        8. Use the attention weights to compute a weighted sum over the value vectors.\n",
    "        9. Flatten the output back to shape [n_q, num_qo_heads * head_dim] and write it to the output.\n",
    "        \n",
    "        The final output is written to self.outputs[\"output\"].tensor.\n",
    "        \"\"\"\n",
    "        Q = self.inputs[\"Q\"].tensor\n",
    "        scale = 1.0 / (self.head_dim ** 0.5)\n",
    "        # Compute group size: how many query heads correspond to one key/value head.\n",
    "        group_size = self.num_qo_heads // self.num_kv_heads\n",
    "\n",
    "        for i in range(len(self.qo_indicies) - 1):\n",
    "            # Retrieve the query slice for this batch element.\n",
    "            start = self.qo_indicies[i]\n",
    "            end = self.qo_indicies[i + 1]\n",
    "            # Q is expected to be flattened as [n_total, num_qo_heads * head_dim];\n",
    "            # extract the sub-tensor corresponding to this batch element.\n",
    "            sub_q = Q[start:end, :]  # shape: [n_q, num_qo_heads * head_dim]\n",
    "            # Reshape queries into separate heads.\n",
    "            # New shape: [n_q, num_qo_heads, head_dim]\n",
    "            sub_q = sub_q.view(-1, self.num_qo_heads, self.head_dim)\n",
    "            \n",
    "            # Retrieve cached keys and values from the external KV cache.\n",
    "            # Expected shapes:\n",
    "            #   sub_k: [n_k, num_kv_heads * head_dim]\n",
    "            #   sub_v: [n_k, num_kv_heads * head_dim]\n",
    "            sub_k, sub_v = self.externals[\"KVCache\"].get(layer, i)\n",
    "            n_k = sub_k.shape[0]\n",
    "            # Reshape keys and values so that the head dimension is explicit.\n",
    "            # New shapes: [n_k, num_kv_heads, head_dim]\n",
    "            sub_k = sub_k.view(n_k, self.num_kv_heads, self.head_dim)\n",
    "            sub_v = sub_v.view(n_k, self.num_kv_heads, self.head_dim)\n",
    "            \n",
    "            # Expand (repeat) the keys and values so that they align with the query heads.\n",
    "            # Each key/value head is repeated group_size times.\n",
    "            # New shapes after repeat: [n_k, num_qo_heads, head_dim]\n",
    "            expanded_k = sub_k.repeat_interleave(group_size, dim=1)\n",
    "            expanded_v = sub_v.repeat_interleave(group_size, dim=1)\n",
    "            \n",
    "            # Compute attention scores.\n",
    "            # Use Einstein summation notation: for each query (q) and head (h), compute dot product with each key (k)\n",
    "            # resulting in scores of shape: [n_q, num_qo_heads, n_k]\n",
    "            scores = torch.einsum(\"qhd,khd->qhk\", sub_q, expanded_k) * scale\n",
    "            \n",
    "            # -------------------------------\n",
    "            # Add Causal Mask to Attention\n",
    "            # -------------------------------\n",
    "            # Determine the number of query tokens.\n",
    "            n_q = sub_q.shape[0]\n",
    "            # Assume that the KV cache has 'past' tokens (from previous timesteps) and new tokens,\n",
    "            # so that n_k = past_length + n_q.\n",
    "            past_length = max(n_k - n_q, 0)\n",
    "            \n",
    "            if past_length > 0:\n",
    "                # For the new tokens (the last n_q keys), build a lower-triangular mask.\n",
    "                # For each query position i (0-indexed among the new tokens), allow attending only\n",
    "                # to new keys with positions <= i.\n",
    "                new_mask = torch.tril(torch.ones(n_q, n_q, dtype=torch.bool, device=scores.device))\n",
    "                # For the past tokens (first past_length keys), we allow full attention.\n",
    "                past_mask = torch.ones(n_q, past_length, dtype=torch.bool, device=scores.device)\n",
    "                # Concatenate the masks along the key dimension.\n",
    "                causal_mask = torch.cat([past_mask, new_mask], dim=1)  # shape: [n_q, n_k]\n",
    "            else:\n",
    "                # If there is no past context (i.e. n_k == n_q), use a standard lower-triangular mask.\n",
    "                causal_mask = torch.tril(torch.ones(n_q, n_k, dtype=torch.bool, device=scores.device))\n",
    "            \n",
    "            # Expand the causal mask to match the scores' shape: [n_q, num_qo_heads, n_k].\n",
    "            # Then mask out disallowed (future) positions by setting them to -inf.\n",
    "            scores = scores.masked_fill(~causal_mask.unsqueeze(1), float(\"-inf\"))\n",
    "            \n",
    "            # Apply softmax over the key dimension.\n",
    "            attn_weights = torch.softmax(scores, dim=-1)\n",
    "            \n",
    "            # Compute attention output as the weighted sum over the value vectors.\n",
    "            # Resulting shape: [n_q, num_qo_heads, head_dim]\n",
    "            out = torch.einsum(\"qhk,khd->qhd\", attn_weights, expanded_v)\n",
    "            # Flatten heads back to shape: [n_q, num_qo_heads * head_dim]\n",
    "            out = out.reshape(-1, self.num_qo_heads * self.head_dim)\n",
    "            # Write the computed output into the operator's output tensor.\n",
    "            self.outputs[\"output\"].tensor[start:end, :].copy_(out)\n",
    "\n",
    "\n",
    "\n",
    "class GEMM(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"A\": IOWrapper(self, 'A', IOBufferType.FULL),\n",
    "            \"C\": IOWrapper(self, 'C', IOBufferType.FULL)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"D\": IOWrapper(self, 'D', IOBufferType.FULL)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"B\": WeightWrapper()\n",
    "        }\n",
    "        \n",
    "    def setShape(self, N, K):\n",
    "        self.N = N\n",
    "        self.K = K\n",
    "        self.weights[\"B\"].shape = (self.K, self.N)\n",
    "    \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"A\"].shape = (self.M, self.K)\n",
    "        self.inputs[\"C\"].shape = (self.M, self.N)\n",
    "        self.outputs[\"D\"].shape = (self.M, self.N)\n",
    "        \n",
    "    \n",
    "    def run(self, layer):\n",
    "        A = self.inputs[\"A\"].tensor\n",
    "        C = self.inputs[\"C\"].tensor\n",
    "        B = self.weights[\"B\"].weight_map[layer]\n",
    "        self.outputs[\"D\"].tensor.copy_(A.matmul(B) + C)\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached = False):\n",
    "        return process_weight_layer(global_weight_map, self.weight_name, self.weights[\"B\"], total_layers, cached)\n",
    "\n",
    "class Activation(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"input\": IOWrapper(self, 'input', IOBufferType.FULL),\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.FULL)\n",
    "        }\n",
    "        self.act_fn = torch.nn.SiLU()\n",
    "        \n",
    "    def setShape(self, N):\n",
    "        self.N = N\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"input\"].shape = (self.batch_size, self.N * 2)\n",
    "        self.outputs[\"output\"].shape = (self.batch_size, self.N)\n",
    "        \n",
    "    def run(self, layer):\n",
    "        x = self.inputs[\"input\"].tensor\n",
    "        # Split the input along the last dimension.\n",
    "        A, B = torch.split(x, self.N, dim=-1)\n",
    "        self.outputs[\"output\"].tensor.copy_(A * self.act_fn(B))\n",
    "\n",
    "class Sampling(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"logits\": IOWrapper(self, 'logits', IOBufferType.FULL)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"tokens\": IOWrapper(self, 'tokens', IOBufferType.FULL, dtype=torch.int32)\n",
    "        }\n",
    "    \n",
    "    def setShape(self, vocab_size):\n",
    "        self.vocab_size = vocab_size\n",
    "        \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"logits\"].shape = (self.batch_size, self.vocab_size)\n",
    "        self.outputs[\"tokens\"].shape = (self.batch_size,)\n",
    "        \n",
    "    def run(self, layer):\n",
    "        logits = self.inputs[\"logits\"].tensor\n",
    "        tokens = torch.argmax(logits, dim=1)\n",
    "        self.outputs[\"tokens\"].tensor.copy_(tokens)\n",
    "\n",
    "class GlobalOutput(Operations):\n",
    "    def __init__(self, name):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"tokens\": IOWrapper(self, 'tokens', IOBufferType.FULL, dtype=torch.int32)\n",
    "        }\n",
    "        self.outputs = {}\n",
    "    \n",
    "    def setBatchSize(self, batch_size):\n",
    "        self.batch_size = batch_size\n",
    "        self.inputs[\"tokens\"].shape = (self.batch_size,)\n",
    "\n",
    "    def run(self, layer):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import matplotlib.patches as patches\n",
    "import networkx as nx\n",
    "def draw_graphs_subplots(graphs, title_prefix):\n",
    "    \"\"\"Draw each graph from a list of NetworkX graphs on subplots with bounding boxes.\"\"\"\n",
    "    num_graphs = len(graphs)\n",
    "    cols = math.ceil(math.sqrt(num_graphs))\n",
    "    rows = math.ceil(num_graphs / cols)\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(cols * 4, rows * 4))\n",
    "    \n",
    "    # Flatten axes so we can index by a single index.\n",
    "    if num_graphs == 1:\n",
    "        axes = [axes]\n",
    "    else:\n",
    "        axes = axes.flatten()\n",
    "\n",
    "    for i, graph in enumerate(graphs):\n",
    "        ax = axes[i]\n",
    "        ax.set_title(f\"{title_prefix} {i}\")\n",
    "        pos = nx.spring_layout(graph)\n",
    "        nx.draw(graph, pos, with_labels=True, font_weight='bold', ax=ax)\n",
    "        # Compute bounding box based on node positions.\n",
    "        x_vals = [p[0] for p in pos.values()]\n",
    "        y_vals = [p[1] for p in pos.values()]\n",
    "        if x_vals and y_vals:\n",
    "            x_min, x_max = min(x_vals), max(x_vals)\n",
    "            y_min, y_max = min(y_vals), max(y_vals)\n",
    "            margin_x = 0.1 * (x_max - x_min) if (x_max - x_min) != 0 else 0.1\n",
    "            margin_y = 0.1 * (y_max - y_min) if (y_max - y_min) != 0 else 0.1\n",
    "            rect = patches.Rectangle(\n",
    "                (x_min - margin_x, y_min - margin_y),\n",
    "                (x_max - x_min) + 2 * margin_x,\n",
    "                (y_max - y_min) + 2 * margin_y,\n",
    "                edgecolor='red',\n",
    "                facecolor='none',\n",
    "                lw=2\n",
    "            )\n",
    "            ax.add_patch(rect)\n",
    "\n",
    "    # Turn off unused axes.\n",
    "    for j in range(i + 1, len(axes)):\n",
    "        axes[j].axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "def plot_graph_topological(G):\n",
    "    # Compute the topological order\n",
    "    topological_order = list(nx.topological_sort(G))\n",
    "    \n",
    "    # Assign x-coordinates based on topological depth\n",
    "    depth_map = {}\n",
    "    for node in topological_order:\n",
    "        depth_map[node] = max((depth_map.get(pred, -1) for pred in G.predecessors(node)), default=-1) + 1\n",
    "    \n",
    "    # Group nodes by depth\n",
    "    depth_levels = {}\n",
    "    for node, depth in depth_map.items():\n",
    "        depth_levels.setdefault(depth, []).append(node)\n",
    "    \n",
    "    max_depth = max(depth_map.values())\n",
    "    \n",
    "    # Assign positions\n",
    "    pos = {}\n",
    "    max_node_at_depth = max(len(nodes) for nodes in depth_levels.values())\n",
    "    for depth, nodes in depth_levels.items():\n",
    "        y_offset = -(len(nodes) - 1) / 2  # Center nodes vertically\n",
    "        for i, node in enumerate(nodes):\n",
    "            pos[node] = (depth, y_offset + i)\n",
    "    \n",
    "    # Create label positions by shifting each label upward\n",
    "    label_pos = {node: (x, y + 0.2 * (-1)**(x)) for node, (x, y) in pos.items()}\n",
    "    \n",
    "    # set y limit to be 1.2x the number of nodes\n",
    "    \n",
    "    # Plot the graph\n",
    "    plt.figure(figsize=( 5 + 1 * max_depth, 2 + max_node_at_depth))\n",
    "    nx.draw(\n",
    "        G, pos, with_labels=False, node_size=1000, node_color=\"skyblue\", font_size=10, font_color=\"black\"\n",
    "    )\n",
    "    plt.ylim(-max_node_at_depth*0.6, max_node_at_depth * 0.6)\n",
    "    nx.draw_networkx_labels(G, label_pos, font_size=10, font_color=\"black\")\n",
    "    plt.title(\"Graph Layout by Topological Depth\", fontsize=16)\n",
    "    plt.show()\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "class BufferAllocator():\n",
    "    def __init__(self, buffers_list):\n",
    "        self.buffers_list = buffers_list\n",
    "        self.alloc_nodes = {}\n",
    "        self.allocation_graph = []\n",
    "        self.total_allocated = 0\n",
    "\n",
    "    def check_buffer_name_and_size(self):\n",
    "        # Assert fullName is unique.\n",
    "        fullName_list = [wrapper.fullName for wrapper in self.buffers_list]\n",
    "        assert len(fullName_list) == len(set(fullName_list))\n",
    "\n",
    "        for wrapper in self.buffers_list:\n",
    "            assert wrapper.shape is not None, f\"{wrapper.fullName} has no shape\"\n",
    "            wrapper.checkCorrectPartition()\n",
    "    \n",
    "    def create_dependency_graph(self):\n",
    "        # Build full graph G.\n",
    "        G = nx.DiGraph()\n",
    "        for wrapper in self.buffers_list:\n",
    "            G.add_node(wrapper.fullName, wrapper=wrapper)\n",
    "        for wrapper in self.buffers_list:\n",
    "            for next_wrapper in wrapper.next:\n",
    "                G.add_edge(wrapper.fullName, next_wrapper.fullName)\n",
    "        self.full_graph = G\n",
    "\n",
    "    def draw_dependency_graph(self):\n",
    "        # Draw full graph.\n",
    "        plot_graph_topological(self.full_graph)\n",
    "        plt.show()\n",
    "\n",
    "    def get_connected_components(self):\n",
    "        # Get connected components.\n",
    "        components = list(nx.weakly_connected_components(self.full_graph))\n",
    "        return components\n",
    "    \n",
    "    def draw_dependency_subgraphs(self):\n",
    "        # Build list of subgraphs from G.\n",
    "        G_subgraphs = [self.full_graph.subgraph(comp) for comp in self.get_connected_components()]\n",
    "        draw_graphs_subplots(G_subgraphs, title_prefix=\"Buffer\")\n",
    "\n",
    "    def create_allocation_graph_from_component(self):\n",
    "        components = self.get_connected_components()\n",
    "        for component in components:\n",
    "            wrappers = [self.full_graph.nodes[name]['wrapper'] for name in component]\n",
    "            G_H = nx.DiGraph()\n",
    "            for wrapper in wrappers:\n",
    "                G_H.add_node(wrapper.fullName, wrapper=wrapper)\n",
    "            # Use 'child' attribute to get connections.\n",
    "            # If child does not exist, adjust to the correct attribute.\n",
    "            for wrapper in wrappers:\n",
    "                for child in wrapper.child:\n",
    "                    G_H.add_edge(wrapper.fullName, child.fullName)\n",
    "            \n",
    "            # find all the nodes that have no incoming edges\n",
    "            semi_root_nodes = [G_H.nodes[node]['wrapper'] for node in G_H.nodes if G_H.in_degree(node) == 0]\n",
    "            # create a new node \n",
    "            alloc_node = IOWrapper(\"Alloc\", semi_root_nodes[0].fullName, IOBufferType.FULL, dtype=semi_root_nodes[0].dtype)\n",
    "            alloc_node.shape = semi_root_nodes[0].shape\n",
    "            alloc_node.child = semi_root_nodes\n",
    "                \n",
    "            self.alloc_nodes[frozenset(component)] = alloc_node\n",
    "            \n",
    "            # # also add the new node to the graph\n",
    "            G_H.add_node(alloc_node.fullName, wrapper=alloc_node)\n",
    "            for node in semi_root_nodes:\n",
    "                G_H.add_edge(alloc_node.fullName, node.fullName)\n",
    "            self.allocation_graph.append(G_H)\n",
    "    \n",
    "    def draw_allocation_subgraphs(self):\n",
    "        draw_graphs_subplots(self.allocation_graph, title_prefix=\"Allocation\")\n",
    "    \n",
    "    def allocate_buffers_for_components(self):\n",
    "        self.total_allocated = 0\n",
    "        for comp, alloc_node in self.alloc_nodes.items():\n",
    "            shape = alloc_node.shape\n",
    "            tensor = torch.empty(shape, dtype=alloc_node.dtype, device='cuda')\n",
    "            self.total_allocated += tensor.numel() * tensor.element_size()\n",
    "            alloc_node.tensor = tensor\n",
    "            for semi_root in alloc_node.child:\n",
    "                semi_root.tensor = tensor\n",
    "                assert semi_root.shape == shape, f\"shape mismatch: {semi_root.fullName} {semi_root.shape} vs {shape}\"\n",
    "                # if have child then further propagate\n",
    "                if semi_root.child:\n",
    "                    size_for_child = [child.shape[0] for child in semi_root.child]\n",
    "                    assert sum(size_for_child) == shape[0], f\"shape mismatch: {semi_root.fullName} {size_for_child} vs {shape}\"\n",
    "                    tensor_split_list = tensor.split(size_for_child, dim=0)\n",
    "                    for idx, child, tensor_split in zip(range(len(semi_root.child)), semi_root.child, tensor_split_list):\n",
    "                        child.tensor = tensor_split\n",
    "                        child.tensor_offset = sum(size_for_child[:idx])\n",
    "                        assert child.shape == tensor_split.shape, f\"shape mismatch: {child.fullName} {child.shape} vs {tensor_split.shape}\"\n",
    "    \n",
    "    def allocate_buffer(self, plot = False):\n",
    "        self.check_buffer_name_and_size()\n",
    "        self.create_dependency_graph()\n",
    "        self.create_allocation_graph_from_component()\n",
    "        self.allocate_buffers_for_components()\n",
    "        if plot:\n",
    "            self.draw_dependency_graph()\n",
    "            self.draw_dependency_subgraphs()\n",
    "            self.draw_allocation_subgraphs()\n",
    "        return self.total_allocated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Executor():\n",
    "    def __init__(self, operations_list, layer):\n",
    "        self.operations_list = operations_list\n",
    "        self.layer = layer\n",
    "        self.ordered_operations = []\n",
    "    \n",
    "    def not_this_layer(self, op, layer):\n",
    "        return (op.first_layer_only and layer != 0) or (op.last_layer_only and layer != self.layer - 1)\n",
    "    \n",
    "    def plan_layer_ordering(self):\n",
    "        G = nx.DiGraph()\n",
    "        for op in self.operations_list:\n",
    "            for l in range(self.layer):\n",
    "                if (not l == 0 and op.first_layer_only) or (not l == self.layer - 1 and op.last_layer_only):\n",
    "                    continue\n",
    "                G.add_node(f\"{op.name}_{l}\", op=op, layer=l)\n",
    "        for op in self.operations_list:\n",
    "            for i in range(self.layer):\n",
    "                if (self.not_this_layer(op, i)):\n",
    "                        continue\n",
    "                for dep, dep_on_prev_layer in op.prerequisites:\n",
    "                    if (self.not_this_layer(dep, i)):\n",
    "                        continue\n",
    "                    if dep_on_prev_layer:\n",
    "                        if i > 0:\n",
    "                            G.add_edge(f\"{dep.name}_{i - 1}\", f\"{op.name}_{i}\")\n",
    "                    else:\n",
    "                        G.add_edge(f\"{dep.name}_{i}\", f\"{op.name}_{i}\")\n",
    "        self.ordered_operations = list(nx.topological_sort(G))\n",
    "        self.ordered_graph = G\n",
    "    \n",
    "    def draw_ordered_graph(self):\n",
    "        plot_graph_topological(self.ordered_graph)\n",
    "    \n",
    "    def execute(self, weight_map):\n",
    "        for op_name in self.ordered_operations:\n",
    "            op, layer = self.ordered_graph.nodes[op_name]['op'], self.ordered_graph.nodes[op_name]['layer']\n",
    "            op.run(layer)\n",
    "    \n",
    "    def print_debug(self, filename=\"out.txt\"):\n",
    "        file = f\"{filename}\"\n",
    "\n",
    "        with open(file, \"w\") as f:\n",
    "            for op_name in self.ordered_operations:\n",
    "                op, layer = self.ordered_graph.nodes[op_name]['op'], self.ordered_graph.nodes[op_name]['layer']\n",
    "                print(f\"{op.name}_{layer}\")\n",
    "\n",
    "                for inputs in op.inputs.values():\n",
    "\n",
    "                    f.write(f\"[{op.name}_{layer}_{inputs.name}]\\n\")\n",
    "                    f.write(str(inputs.tensor))\n",
    "                    f.write(\"\\n\")\n",
    "                    f.write(str(inputs.tensor.shape))\n",
    "                    f.write(\"\\n\")\n",
    "                    torch.save(inputs.tensor.cpu(), f\"./out/{op.name}_{layer}_{inputs.name}\")\n",
    "                    \n",
    "\n",
    "                for weights in op.weights.values():\n",
    "                    f.write(f\"[{op.name}_{layer}_{weights.name}]\\n\")\n",
    "                    f.write(str(weights.weight_map[layer]))\n",
    "                    f.write(\"\\n\")\n",
    "                    f.write(str(weights.weight_map[layer].shape))\n",
    "                    f.write(\"\\n\")\n",
    "                    torch.save(weights.weight_map[layer].cpu(), f\"./out/{op.name}_{layer}_{weights.name}\")\n",
    "\n",
    "                f.flush()\n",
    "\n",
    "                op.run(layer)  # Execute the operation\n",
    "\n",
    "                for outputs in op.outputs.values():\n",
    "                    f.write(f\"[{op.name}_{layer}_{outputs.name}]\\n\")\n",
    "                    # f_output.write(str(outputs.tensor))  # Binary as hex\n",
    "                    f.write(str(outputs.tensor))\n",
    "                    f.write(\"\\n\")\n",
    "                    f.write(str(outputs.tensor.shape))\n",
    "                    f.write(\"\\n\")\n",
    "                    torch.save(outputs.tensor.cpu(), f\"./out/{op.name}_{layer}_{outputs.name}\")\n",
    "\n",
    "                f.flush()\n",
    "            f.close()\n",
    "                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "import os\n",
    "import safetensors\n",
    "class WeightManager():\n",
    "    def load_tensors(tensor_path):\n",
    "        original_tensors ={}\n",
    "        for file in tqdm.tqdm(os.listdir(tensor_path)):\n",
    "            if file.endswith(\".safetensors\"):\n",
    "                tensors = safetensors.safe_open(os.path.join(tensor_path, file), 'pt')\n",
    "                for name in tensors.keys():\n",
    "                    tensor = tensors.get_tensor(name)\n",
    "                    original_tensors[name] = tensor\n",
    "        return original_tensors\n",
    "\n",
    "\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def load_from_safe_tensor(self, tensor_path):\n",
    "        self.weight_map = WeightManager.load_tensors(tensor_path)\n",
    "        # make all the tensor fp16\n",
    "        for key in self.weight_map.keys():\n",
    "            self.weight_map[key] = self.weight_map[key].half().to('cuda')\n",
    "    \n",
    "    def set_weight(self, operation_list, total_layers):\n",
    "        for op in operation_list:\n",
    "            op.processWeight(self.weight_map, total_layers)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[128000, 13347, 11, 889, 527, 499, 30]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:00<00:00, 1187.55it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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SuVC3bt0AGP+F2542GUbbxGXcVdder732GjZs2AB/f398+umnKCwsRENDg/SeDUH4wvfs7OyMcePGobm5WZqoGGg9kBh+0bZ2joC2z5MleHt7tzoS21p7BwwYgJiYGOzfv1/qHE+fPo2NGzeajDh2lCs9v5f6Pjv682zreAqFAr6+vkbHM/wBZ/iDzpyLrTOnuroaANo9eWtVVRUmTJiAc+fOYcqUKdi9ezcqKyuh0+mMRjFb+53RmQx3Z55/Tg2BsqKiAjt37jT7lZ2dDaDl7vgLXc7PQ3tZ8vcgXX0Ma11M3759AbSUP3Q6Xae9jlarlabK+OabbzB69GgEBgbC3t5eWl9WVnbRY7R2e3l5eTmAS+84robPPvsMQMtt9pMnT0ZYWBgcHByk9SdOnGh13wtLoQcPHsT+/fvh7++Pm266yWhbFxcXAP87F+acOnUKgPF5MowStfaLurUR145y5swZ6PV6s+su9rkawqrh3Hz++edobm7GbbfdZjTi2FEu9/waXOr7vNLXu1BbxxNCSD9fhuMZOveLjbhcanAwHLu9j0bavHkzKisr0bdvX6xatQrXX389PDw8pIBxsZ+fzrZjxw4ALRMAGxjO86RJk6Q/yFr7Mje1xuX+PBBdiGGti7nlllvg4uKCU6dOYf369Z32OhUVFairq4OXlxfi4uJM1mdlZbUZFg0llNaWx8bGXnlDO5hhxPKGG24wWXfmzBmzpRCDG264AfHx8fjzzz+RlZUlXSd49913w8bGxmhbw3svKipqtXM1/EV//nkydMitBeH8/PxW29cRz8lsbm42O+8UcPHPderUqbCxscFnn30GrVYrnZvOKIGe34acnJxWg62582twqe/T8P+HDh0yu09NTY0UVNrzfd/W8Y4cOYKmpibY2NhI5bLo6GgolUqcPn0apaWlZvczjGy2l6EU39rcZBcy/Pz07dvX7PebJa5VA4B9+/Zh7969AIARI0ZIyw1l5qysrMs67uX8PPB5tWQOw1oX4+npiRkzZgBoucj4YhN0XgknJycALWUQc8P/ixcvbvMYK1asuOjyYcOGXUELO4fhfRtGQc63ZMmSNgOq4cLujz/+WBqlMxdIEhISEBoaioaGBnz00Ucm60tKSrBu3ToAwPDhw6Xl3t7ecHd3R319vRQ2zmfuWAaG92bu87wU5j7XiooKfPnllwDMf66BgYEYNmwYysrKsGTJEuzbt8/siGNH6devH1QqFU6cOCFdJH++P/74A7t27YJCocDQoUPNHuNS3qfhM/ryyy/Njji///77aGxsRFhYmNk/fi5kON6HH36IhoYGk/WGibHT09OlAO/q6orrr78eAMzeUHTu3Lk2r8W8kOGGhE8++aRd15ld7OfnzJkz+Pjjjy/p9TvC2bNncc899wBouUzk/JG1/v37w8fHB5mZmZc8ObTBpf48dNTPIXUxV/fmU7oaGhsbpclG/f39xccff2wyF1pTU5P48ssvpTl5DBPdGhhueb9wbqrzJSUlCQDioYceEo2NjUKIlrmHXnvtNaFUKqWpJS68Bf38edbmzZtnNM+aYQJZV1dXcfLkyUtq0z333GP2vVzMpU7dMWLECOm2eMM51ev1YvXq1cLOzk66Hb+1NpaWlgpbW1tp8soL51Y7n2GeNTc3N/HDDz9Iy8vKyqSpQPr06WOy37hx4wQAMWzYMKPPfdWqVdJ0FeZ+9A3v7XLmZzp/Xil7e3ujSY0Nc3gZ3u+F80oZGOYAM5ybK5lb7XyG99vaPGtBQUFG0zLk5+eLxMREAUCMHz++Q96nXq8XvXr1EgDEoEGDxKlTp6R133//vXBxcTF77tszz9r48eONPuc1a9ZI57C1edacnJzEf/7zH2n52bNnxU033XRZ86wZ5o+7/vrrTSYDzsjIMJpnzTDJsZ2dndi6dau0vKSkRAwcOFD6+bnwdTpj6o5z586JVatWSb+P/P39xYkTJ0y2W7FihQBa5kn8+uuvTb5/Dx48KObPny927Nhh0t7L+Xlo6+fwYhMUn//al3quSN4Y1rqo2tpaqdM2/HKMj48XvXv3FlFRUUad9rBhw0RhYaHR/u0Ja//5z3+EQqEQAKSnEBjmxXruuedanS/owicYeHt7i169ekn7KpVKo0kk29umKwlrtra2wtvbu9WvZ599VgjR0tkYZhh3c3MT1113nQgMDBQAxOTJk9t13m699Vbp3J8/t9qFLnyCQXR0tNETDEJDQ02eYCCEEDk5OVLn7+zsLHr06CHNEP/uu++2GtY+/fRTaV337t3FwIEDxcCBA8X+/fvbPI/mZmwPCwsTPXv2FE5OTgKA8Pb2Npk09XyNjY3S9wCucG6187UW1jQajRg8eLC0PjExUaSmpkoTyqamprbrCQbtfZ9HjhyR/jhycHAQPXr0ENHR0dLrT548+ZKfYODu7i59zj179pTm5gPMz8QvhPETDCIjI6W2e3p6imeffVYAENOmTWt3O44fPy7i4uKkY8bGxorrrrtOeirHhfsYJsw2fE+r1Wpha2srXF1dpbnOOjqshYSESE8C6N27t4iOjpaeVAFADB48WBQVFbV6nKeeekra1svLS/Tq1cvoKQ0AxObNm82291K/T9r6OWRYuzYxrHVxu3fvFg8//LBITEwU7u7uUii5/vrrxdy5c41mbj9fe0KHEEJ899134oYbbhBOTk7C1dVV9OnTR/zzn/8UQrQ+ueP5y7/77jsxYMAA4erqKlxcXMTf/vY38csvv1xWm64krLX19dhjj0n77NmzRwwdOlS4uLgIZ2dnoVarxbJly4Rer2/Xefv6668F0PLIoQufhHAhvV4vPv30U9G/f3/h5uYmHBwcRExMjJg3b95FJyHdt2+fuOmmm4Srq6twdnYWN9xwg/jvf/8rhGh9UlwhhHj77bdFSkqK1KG053tACOMOQq/Xi7ffflt0795dODo6Ch8fHzFp0iSTPwjMmTVrljTi0FEu9j6amprE22+/LXr27CmcnZ2Fk5OTSE5OFgsXLjR6aofBlb7PiooK8cQTT4iYmBjh4OAg3NzcxIABA8SaNWvMjji21TEXFhaKhx56SISFhQl7e3vh6ekphg0bJjZu3NhqG3Q6nVixYoVITk4WDg4OwtfXV4wbN04cOXJEvPPOOybf7+1pR21trXj11VdFjx49hIuLi1CpVCImJkbcc889Yvv27UbbNjY2iueee06Eh4cLOzs74e/vLyZMmCByc3NbfZ0rDWvnf6lUKhEQECD69+8v5s6dK37//fd2HWvnzp1i4sSJIiQkRNjb2wsvLy+RkpIipk2bJjZu3Gj0dJIr/T652M8hw9q1SSEE7++lqys8PBzHjx9HQUHBRR843lW99957eOSRRzB27FjpuhVqMWHCBPz73//GO++8Y3YyUUsrLCxEREQEwsLCWp0ex5rNnDkT77zzDt566y3Mnj3b0s2xWl39+4SuPt5gQHSVGS6iNjeL/LXszJkz+Oabb+Dg4NApc6vRxdXW1uKrr74CAKOJs4nI8hjWiK6idevW4Y8//kBkZGSn3elorRYsWICGhgZMmDChU+ZWoxZLly5FRkaG0bKTJ09izJgx0qOVDI8sIiJ54OOmiK6CQYMGoaamBvv37wcALFy48KLPXL1WZGRkYPbs2SgpKcGRI0fg5OSE5557ztLN6tI2bNiAxx9/HO7u7oiMjERjYyNyc3Oh1+vh4+Nj8vxaIrI8hjWiq+CXX36BjY0NIiMjMXfuXJb5/lJVVYVffvkFDg4O6NWrFxYvXtzqMw+pYzz22GPw9PTEvn37kJubCyEEoqKicPPNN2P+/PnSY+KISD54gwERERGRjLEOQ0RERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMsawRkRERCRjDGtEREREMmZr6QYQERERXYwQAhqtQLNeQC8ApQKwUyqgslVAoVBYunmdjmGNiIiIZEWj1eN4TTPKNFqUalr+26Q33c5eCfirbBGgsoO/yhZhrnZQ2Xa9oqFCCCEs3QgiIiK6tgkhUKLRYl9FPXIqm6BHy7VaZjKaCcN2SgAJnvbo4euEQJVtlxl1Y1gjIiIii8qrasT2Ug1ON+igAHAlwcSwv6+jDQYEqhDj7tAxjbQghjUiIiKyiHqtHluKa5FT2XTFIe1ChuMleNpjWLALnKy4PMqwRkRERFddXlUjNhfVokEnOjSkXUgBwNFGgZtDXRDrYZ2jbAxrREREdNUIIbDrVD22l2qu+msPDFChTzcnq7uWjWGNiIiIrgohBH4p1WD3qXqLtaFvNycMCFBZVWCz3gIuERERWZVdp+otGtTk0oZLxbBGREREnc5wx6cc/FKqQV5Vo6Wb0W4Ma0RERNSp6rV6bC6qtXQzjGwuqkW9tj2zuFkewxoRERF1qi3FLXd9ykmDTmBrsbwCZGsY1oiIiKjT5FU1IqeyqVOn57gcAsChyiYcOSf/cijDGhEREXUKIQS2l2og1/suFQC2l2gg94kxGNaIiIioU5RotDjdoJPdqJqBAFDRoEOJRmvpplwUwxoRERF1in0V9bIdVTNQoKWdcsawRkRERB1Oo9XL8lq1CwkAOZVN0Mj4zlCGNSIiIupwx2uaId/4Y0wPoKim2dLNaBXDGhEREXW4Mo3WakKGEi3tlStrOY9ERERkRUo11jWyJuebDBjWiIiIqEMJIWQ9UmVOmUYr2yk8GNaIiIioQ2m0Ak3WMqz2lya9gEbLsEZERETXgGa9PENPW7QcWSMiIqJrgZVmNehkOhrIsEZERESXrKGhAfX15ieTVcp9JtxW2Mg0Fcm0WURERCRnQ4cOhYeHB4YPH45ly5bhyJEj0jo7K01rtgp5tpthjYiIiC6Zr68vmpqa8MMPP+Dxxx9HbGws/P39ceONN+JI9kHYW1nCsFcqoLJlWCMiIqIuoKysDC4uLlAqldDr9dDrWy72OnXqFH766Sd8/vm/4K+ytXArL42/yhYKmY6sWdeZJCIioqtGp9MhPz8fGRkZ0tf+/ftx6tQpk22VSiVsbW3x3nvv4d5778W2k3UortVaxcS4SgCBMg6X8m0ZERERXTUajQYHDx40CmYHDhyARqMBAAQHB0OtVuOBBx6AWq2Gn58fBgwYAKAlqEVFRWHDhg1ITEwE0DJSZQ1BDWh5goGcRwLl2zIiIiLqFOXl5UYjZRkZGcjLy4Ner4eNjQ3i4+ORlpaGsWPHQq1WIzU1FT4+PkbHEELAzc0N1dXVmDBhAj744AM4OztL68Nc7aAErCKwKQGEutpZuhmtUgi5PluBiIiIroherzcpY2ZkZKC0tBQA4OLigtTUVKjVaukrKSkJTk5O7Tr++++/DycnJ0yePNns9V7/LazGocomyDloKAAketrj1nA3SzelVQxrREREXUB9fb3ZMmZdXR0AICgoyCiUqdVqREZGQqnsvHsNT9Y1Y03euU47fkeZHOuOIGf5jqyxDEpERGRlKioqTEbLcnNzodfroVQqER8fD7VajTvuuEMqY/r5+V31dgaqbOHraIPTDTpZjq4pAPg42sj65gKAI2tERESypdfrcfToUZNgVlJSAgBwdnY2KWN279693WXMq+HIuUasO1Zj6Wa0akykK2LcHSzdjItiWCMiIpKBhoYGZGVlGYWyzMxM1NbWAgACAgKMQllaWhpiY2Oxfv16jBo1ql2vMXXqVFRVVWHDhg2X3c7CwkJERERg//79UKvV7drnm8Jq5Mrs2jUFgARPe9zWSdeqKRSKS/psLoaT4hIREV1lp0+fxg8//IA33ngDd999N7p37w4XFxf06tULDz30EH766Sf4+voiNTUVAQEBcHBwgE6nQ3V1NUJDQzFy5EjExMRY+m0YWb16NXr37g1nZ2e4urpiwIAB+PbbbwEAw4Jd4GjTvglnv3xhBtbMmdIpbfzhvcVYNmEQAMDRRoGhwS5t7qNQKK4o3HYEeRdpiYiIrJher0dBQYFJGbO4uBgAoFKpkJKSgv79+2PWrFlSGbOsrAzp6enw8PDA0qVLkZycDK1Wi7y8PHzyyScIDAzEbbfdZuF39z9PPPEE3nnnHSxcuBCjRo1Cc3Mz/vnPf+L222/H22+/jRkzZuDmUBd8XSCfcujNoS5wsrWSMStBREREV6y+vl788ccf4qOPPhIzZswQ/fr1E66urgKAACD8/f3FTTfdJJ566inxxRdfiNzcXKHVas0ea/jw4SI4OFjU1taaXa/X64UQQgAQ69evl5YfOHBADB48WDg6OgovLy/xwAMPiJqaGmn9PffcI26//XaxYMEC4evrK1xdXcWDDz4oGhsbpW02b94s0tPThbu7u/Dy8hIjRowQ+fn50vqCggIBQOzfv18IIcSuXbsEALFs2TKTds6ZM0fY2dmJoqIiIYQQ0+Y+IwJik8Sr+yqkrxFzXxIeASHi1X0V4sYH50nny/D1wAcbxPxv/xQAxIRXPxChKb2Erb2D8IuMEw98sEE6ztgFy4Sji5vRse9esloAkNZfeOyVK1de9DMNCwsz2j4sLExat2LFChEZGSns7OxEbGys+PTTT432vfCzefHFF4Wfn5903nbu3Cn69+9/0dc34MgaERHRJTpz5gwyMzONJpbNycmBTqeDQqFAbGws0tLSMHLkSOluTH9//3Yfe8uWLXjllVeMJpk9n7k5zTQaDW666Sb06dMHe/fuRXl5Oe6//37MmDEDq1atkrb78ccf4ejoiG3btqGwsBD33nsvfHx88PLLLwMA6urqMGfOHCQnJ6Ourg7PP/887rjjDmRkZJid5uPzzz+Hi4sLHnroIZN1c+fOxZtvvol169Zh9uzZCHa2heoio1n9p0xHeUEeGutqMHbBMgCAk7snairKAACbly7AyCcWwi8iDjs+exefPn435v33Tzh7eLV+Qv+SMmwUTh3NxYnft+G3bT9CoVDA3d39ovvs3bsXfn5+WLlyJW666SbY2NgAANavX4/HHnsMS5cuxZAhQ/Dtt9/i3nvvRXBwMAYPHmx0DCEEZs+ejQ0bNmDHjh2IiYnBwYMHMXz4cLz00kttthtgGZSIiKhVQgizZcwTJ04AAJycnJCSkoL09HQ8+uijUKvVSE5ObjVktUd+fj6EEIiLizNa7uPjg4aGBgDAo48+ikWLFhmt/+yzz1BfX49PP/1Uev133nkHt956KxYtWoRu3boBAOzt7fHJJ59ApVIhKSkJ//jHPzBv3jy89NJLUCqVGDNmjNFxP/74Y/j5+eHQoUPo3r27SXvz8vIQFRUFe3t7k3WBgYFwd3dHXl4egJaQ6WKnxMAAFX4p1Zhs76BygZ2jI3TNTXD16Wayvu/4+9D9xlsBALc//TryfvsJf2z4DAOnzjR/Ms9j5+iEGD8PVDvaIyAgoM3tAcDX1xcA4OHhYRS233jjDUydOhXTp08HAMyZMwe7d+/GG2+8YRTWtFotpkyZgj/++AM7d+5EcHAwAOD111/HxIkTMXv27Ha1g2GNiIgIQGNjIw4dOmT0CKbMzExUV1cDAPz8/JCWloaJEydKd2TGxMRIoy0d7cLRs99//x16vR6TJk1CY2OjyfY5OTlITU01Corp6enQ6/U4fPiwFNZSU1OhUqmkbfr27Yva2lqcOHECYWFhOHr0KJ577jns3r0bp0+fhl7f8sCooqIis2GtLUIIk/fS118Fb0cbbC6qRftuO2gRmtxT+n8bW1sEJapRUZDX5n5ONgrcHOqCf7nY4c9LeL3W5OTk4MEHHzRalp6ejrffftto2eOPPw4HBwfs3r3b6HFdf/75J/Lz8/HZZ59Jd/teDMMaERFdc86ePWtUxszIyMChQ4eg1WqhUCgQExMDtVqNm2++WQpm7R2NuVLR0dFQKBTIzc01Wh4ZGQkArc6hZi4UGbS23Nw2t956K0JCQvDhhx8iMDAQer0e3bt3R1NTk9n9YmNjsWPHDjQ1NZmMrpWUlKC6ulq6c1WpVEL8NWNYrIcDQlzssN+u5d8K4PKm9vir3QqlEuK8IygA6LXNAIAHEz07/GaCC8+pufM/dOhQfP755/j+++8xadIkabler8dDDz2EWbNmteu1GNaIiKjLEkLg+PHj0kiZ4auoqAgA4OjoiJSUFPTp0wcPP/ywVMZ0cWl7SofO4u3tjaFDh+Kdd97BzJkz211STUxMxOrVq1FXVyfts3PnTiiVSsTGxkrbZWZmor6+Xgp9u3fvhouLC4KDg3HmzBnk5OTg/fffR//+/QEAO3bsuOjrTpgwAcuWLcP777+PmTONy5FvvPEG7OzspNKqr68vysrKpGDjZKtEc1EunG0V8HG0QUWDDra29tDrdGZfq+jgn4i47gYAgE6rxcmcTPQddx8AwNnTG011tWiur4OdkzN8HG3gVt4y6mYIavb29tC1cuzW2NnZmeyTkJCAHTt2YMqU/00x8ttvvyEhIcFou9tuuw233norJk6cCBsbG0yYMAEA0KNHD2RnZyM6OrpdbWBYIyKiLqGpqUkqY57/de5cy7MpfXx8kJaWhvHjx0uTysbExMDWVn5d4YoVK5Ceno6ePXtiwYIFSElJgVKpxN69e5Gbm4vrrrvOZJ9JkybhhRdewD333IMFCxagoqICM2fOxOTJk6USKNBynu677z78/e9/x/Hjx/HCCy9gxowZUCqV8PT0hLe3Nz744AMEBASgqKgITz311EXb2rdvXzz22GOYN28empqajKbuePvtt7F06VKEhIQAAAYNGoSKigosXrwYY8eOxXfffYfNmzfDzc0N0+I9UKLRIjsqHN/u2oaKwny4uHvC3uV/k9buXvsJfEIj4RsRi52fvYf66ir0vH0ilABCul8HO0cn7P3wNTz5+CwU7tuH/3z+T6O2hoeHS9cgBgcHw9XVFQ4OF396QXh4OH788Uekp6fDwcEBnp6emDdvHsaNG4cePXrgxhtvxH//+198/fXX+OGHH0z2v+OOO7BmzRpMnjwZtra2GDt2LJ588kn06dMHjz76KP7v//7voq8PgFN3EBGR9Tl79qzYtm2beOutt8Q999wjUlNThZ2dnTTFQkxMjLjzzjvFyy+/LDZu3ChOnjwpTXdhLUpKSsSMGTNERESEsLOzEy4uLqJ3797i9ddfF3V1dUKIy5+64/nnnxfe3t7CxcVF3H///aKhoUHaZuvWrSIhIUE4ODiIlJQU8fPPPxu9zoVTdxh8/PHHomfPnsLJyUmoVCrRr18/8Z///Mfkfb377rsiJCREODs7iylTpoiXX37ZaEqM8vJy8bchQ4TKxUUAEM+u+a94dmPL1B3jX3lfhHS/TtjY2Qu/iFjx8Adfi8/yqsS24lqRc7ZBfPHVOhEdHS0cHR3FyJEjxQcffCDOjzoNDQ1izJgxwsPDo11TdwghxH/+8x8RHR0tbG1tr2jqjn//+9/C0dFRrFu3TgghxO+//y6GDh3a5usLIQQfN0VERLIlhEBRUZHJaFlhYSEAwMHBAcnJyUaPYUpJSYGrq6tlG04dqqCgAJGRkdj++x9ISlbDRgnYKhRQ2SradT2etZPf2C8REV2TmpqakJOTYxLMqqqqALRcy5WWloaxY8dKwSwuLk6WZUzqWIZA5mpnAy/Hzrn7Vs74HU5ERFfduXPnTCaVzc7ORnNzy917UVFRUKvVmDt3LtLS0qBWqxEYGHhNjKLQ1fPZZ5+ZncwXAMLCwpCdnX2VW2Qey6BERNRphBA4ceKEyWhZQUEBgJa788yVMd3c3No4MtGVq6mpwalTp8yus7OzQ1hY2FVukXkMa0RE1CGam5uRm5trEszOnj0LAPDy8pJGyc4vY9rZ2Vm45UTyxrBGRESXrLq62mRS2aysLGni1MjISKNQplarERwczDIm0WVgWCMiolYJIXDy5EmjRzBlZGTg2LFjAFrKmN27dzcpY7b1gGwiaj+GNSIiAtBSxjx8+LBJGfPMmTMAAE9PT5PRsoSEBJYxiToZwxoR0TWopqbGbBnT8IDwiIgIk2AWEhLCMiaRBTCsERF1YUIIlJSUmIyW5efnA2i54y0pKUkKZGlpaUhJSYGHh4dlG05EEoY1IqIuQqvVmi1jnj59GgDg4eFhtoxpb29v4ZYT0cUwrBERWaHa2locOHDAKJQdPHgQDQ0NAFom9DSMlBmCWWhoKMuYRFaIYY2ISMaEECgtLTVbxhRCwNbW1qiMqVarkZqaCk9PT0s3nYg6CMMaEZFM6HQ65OXlGT2CKSMjAxUVFQAANzc3o2vLDGVMBwcHC7eciDoTwxoRkQXU1tbi4MGDJmXM+vp6AEBoaKjJ9WXh4eEsYxJdgxjWiIg6WVlZmcmkskeOHJHKmAkJCUbXlqWmpsLLy8vSzSYimWBYIyLqIDqdDkeOHDG5vszwoGhXV1eT0bLExEQ4OjpauOVEJGcMa0REl0Gj0ZjcjXngwAGpjBkSEmK2jKlUKi3cciKyNgxrRERtOHXqlMloWV5eHvR6PWxsbJCQkGByN6aPj4+lm01EXQTDGhHRX3Q6HY4ePWp0bVlGRgbKysoAAC4uLiajZUlJSSxjElGnYlgjomuSRqNBVlaWSRmzrq4OABAUFGQyTUZERATLmER01TGsEVGXV15eblLGPHz4sFTGjI+PNylj+vr6WrrZREQAGNaIqAvR6/U4evSoSTArKSkBADg7OyM1NdVomoykpCQ4OTlZuOVERK1jWCMiq1RfX29SxszMzJTKmIGBgSbXl0VFRbGMSURWh2GNiGSvoqICmZmZRhPL5ubmQq/XQ6lUGpUxU1NToVar4efnZ+lmExF1CIY1IpINvV6PY8eOmZQxT548CQBQqVRSGDN8de/eHSqVysItJyLqPAxrRGQRDQ0NyM7ONpomIzMzE7W1tQCAgIAAs2VMGxsbC7eciOjqYlgjok535swZk9GynJwc6HQ6KBQKxMXFmQSzbt26WbrZRESywLBGRB1Gr9ejsLDQZFLZ4uJiAC1lzJSUFJMyprOzs4VbTkQkXwxrRHRZGhsbkZ2dbXI3ZnV1NQDAz89PmiLD8N/o6GiWMYmILhHDGhG16ezZs2bLmFqtFgqFArGxsSZlTH9/f0s3m4ioS2BYIyKJEAKFhYUmwayoqAgA4OjoiJSUFKNJZZOTk1nGJCLqRAxrRNeoxsZGHDp0yKSMee7cOQCAr6+vUShTq9WIiYmBra2thVtORHRtYVgjugZUVlZKk8oavg4dOoTm5mYoFArExMSYLWMqFApLN52I6JrHsEbUhQghcPz4cZMy5vHjxwG0lDGTk5ONQllKSgpcXFws3HIiImoNwxqRlWpqakJOTo4UyPbv34/MzExUVVUBAHx8fEzKmLGxsSxjEhFZGYY1IitQVVVlUsbMzs5Gc3MzACA6OloKZIaAFhAQwDImEVEXwLBGJCNCCJw4ccJotCwjIwOFhYUAAAcHB7NlTFdXV8s2nIiIOg3DGpGFNDc3G5UxDV+VlZUAAG9vb6ORMrVajbi4OJYxiYiuMQxrRFfBuXPncODAAaPRsuzsbDQ1NQEAoqKiTO7GDAoKYhmTiIgY1og6khACxcXFJqNlx44dAwDY29ubLWO6ublZuOVERCRXDGtEl6m5uRmHDx82eWj52bNnAQBeXl4mo2Xx8fGws7OzcMuJiMiaMKwRtUN1dbVUxjR8ZWVlobGxEQAQGRlpEsyCg4NZxiQioivGsEZ0HiEETp48aVLGPHr0KADAzs4O3bt3NwplqampcHd3t3DLiYioq2JYo2uWVqvF4cOHTYLZ6dOnAQCenp5my5j29vYWbjkREV1LrC6sKRQKrF+/HqNGjUJhYSEiIiKwf/9+qNVqSzftoqZOnYqqqips2LDB0k25JtXU1JiUMQ8ePCiVMcPDw00mlQ0JCWEZk4iILK7dEzYNHz4cCxYsQN++fTuzPZckJCQEpaWl8PHxuWqvydAlb0IIlJaWmoyW5efnQwgBOzs7JCUlQa1W4+6775bKmB4eHpZuOhERkVntDmuDBg2S7nKTCxsbG/j7+7d7eyEENFqBZr2AXgBKBWCnVEBlq+AIihXSarXIy8szCWYVFRUAAA8PD6jVaowYMUIaNUtISGAZk4jIylzr/beyvRs+/fTTGDFiBADgzTffRHJyMpydnRESEoLp06ejtrZW2nbVqlXw8PDAt99+i7i4OKhUKowdOxZ1dXVYvXo1wsPD4enpiZkzZ0Kn00n7hYeH46WXXsLEiRPh4uKCwMBALF++vNU2FRYWQqFQICMjAwDw888/Q6FQ4Mcff0TPnj2hUqmQ1rsvVm/fj38dqcJbB85gedZZjJvzd4QG+qOblzsGjZuCG+97DGEJydh2sg45lY3QaPVmX2/BggVYvXo1vvnmGygULd8gP//8MwDg4MGD+Nvf/gYnJyd4e3vjwQcfNDonF/rzzz/h5+eHl19+GUDLpKkPPvgg/Pz84Obmhr/97W/IzMw0em21Wo01a9YgPDwc7u7umDBhAmpqaqRtvvrqKyQnJ0ttGDJkCOrq6lptgzWpra3Frl278O677+Khhx5C79694erqiqSkJEyaNAlr166FSqXC9OnTsX79ehQUFODs2bPYtm0b3nrrLdxzzz1ITU1lUCMisgIarR45lY3YdrLOqP9+71AlPsipxHuHKrE86yzeOnAG/zpS1Wb/be0u67k1SqUSy5YtQ3h4OAoKCjB9+nTMnz8fK1askLbRaDRYtmwZvvjiC9TU1GD06NEYPXo0PDw8sGnTJhw7dgxjxoxBv379MH78eGm/119/Hc888wwWLFiA77//Ho8//jji4+MxdOjQdrdv3tPP4M4n/oGBdu74+uUn8MKsh/Dwyk0AgP2bvsK2j5fi9qcWIUx9PQ58vx6//nMFvAJDsbe8Hnq0JNgET3v08HVCoMpWSu1PPPEEcnJyUF1djZUrVwJomUtLo9HgpptuQp8+fbB3716Ul5fj/vvvx4wZM7Bq1SqT9v38888YNWoUXn31VTzyyCMQQmDEiBHw8vLCpk2b4O7ujvfffx833ngj8vLy4OXlBQA4evQoNmzYgG+//RaVlZUYN24cXnvtNbz88ssoLS3FXXfdhcWLF+OOO+5ATU0Nfv31V1jZJYkQQqCsrMxktOzIkSMQQsDW1lYqY06cOFEqY3p6elq66UREdAWEECjRaLGvoh45lU1Sf3yx+NWkB4pqtSiu1V60/7Z27Q5rBw4cQEpKCgBg9uzZ0vKIiAi89NJLeOSRR4zCWnNzM959911ERUUBAMaOHYs1a9bg1KlTcHFxQWJiIgYPHoxt27YZhbX09HQ89dRTAIDY2Fjs3LkTb731VrvC2onalkf39Jj2JJRxveELYOC9s7B61kQ0NzbAzsERu774CD1vn4iet08EANz44BM4snsbmjR10jeEHsChyiZkVzbB19EGAwJViHF3gIuLC5ycnNDY2GhUfl29ejXq6+vx6aefwtnZGQDwzjvv4NZbb8WiRYvQrVs3adtvvvkGkydPxvvvv4+77roLALBt2zYcPHgQ5eXlcHBwAAC88cYb2LBhA7766is8+OCDLe3S67Fq1Srpod2TJ0/Gjz/+KIU1rVaL0aNHIywsDACQnJzc5jmzJJ1OhyNHjhg9gikjIwPl5eUAADc3N6jVatx000146qmnoFarkZiYKJ0jIiLqGvKqGrG9VIPTDTooABiGGdo7TtZW/23t2h3WevTogY8++ghTp07Ftm3b8Morr+DQoUOorq6GVqtFQ0MD6urqpLCiUqmkoAYA3bp1Q3h4OFxcXIyWGTpmgwtvYOjbty+WLl160bY1aPX4prAav5bWAwD8YxOlD9rNpyUo1Z09DY+AYFQcz0efcfca7R+c1APH9v5qtMyw/+kGHdYdq0GCZyOGBbvAnJycHKSmpkrvHWgJnXq9HocPH5bC2p49e/Dtt9/iyy+/xB133CFt++eff6K2thbe3t5Gx62vr5fm9wJaysSGoAYAAQEB0vlLTU3FjTfeiOTkZAwfPhzDhg3D2LFjZTPiVFdXh4MHDxqNlh04cAD19S2fWWhoKNRqNR5++GHp+rLw8PAu81cRERGZqtfqsaW4FjmVTTD8tr/SelBr/beTbbuv/JKddoe1qVOn4oUXXsDgwYNxyy234OGHH8ZLL70ELy8v7NixA/fddx+am5ul7S98pI5CoTC7TK9vOze31WGvO1YNH5sm6d82tue9zl/7CnH+61xwvIuUCg1rciubUFhdieomnek2QrTaxvOXR0VFwdvbG5988glGjBghXT+l1+sREBAgXf92vvPvUrzY+bOxscHWrVvx22+/YcuWLVi+fDmeffZZ7NmzBxEREa2+v85w6tQpk0cw5eXlQQgBGxsbJCYmQq1WY9y4cUhLS0NqaqpU6iUiomtDXlUjNhfVokHX0tN29EU7F/bfN4e6INbDOkfZ2h3WEhMTsWHDBvzxxx/QarVYsmQJlMqWlLp27doOa9Du3btN/h0fH2+ynRACf1a0jMo06kW7P2TfsGgUZ+9Dj5HjpGUnD2W0uZ8AUK8TKG5QwF7TZBTQEhMTsXr1aqORxZ07d0KpVCI2NlY6ho+PD77++msMGjQI48ePx9q1a2FnZ4cePXqgrKwMtra2CA8Pb+c7MaVQKJCeno709HQ8//zzCAsLw/r16zFnzpzLPubF6HQ65Ofnm1xfVlZWBgBwdXWFWq3GsGHDMH/+fKmM6ejo2CntISIi+RNCYNepemwv1Vyd10NL//11QQ0GBujQp5uT1VVt2h3WFi9ejNtvvx1RUVHQarVYvnw5br31VuzcuRPvvfdehzVo586dWLx4MUaNGoWtW7fiyy+/xMaNG422EULgl1INfi+vv+Tj951wP9YvnIOgRDXCUnvhwJYNKM0/BK+gsHbt7xkYgt/XbcM/f83ATQnB8PDwwKRJk/DCCy/gnnvuwYIFC1BRUYGZM2di8uTJRterAYCfnx9++uknDB48GHfddRe++OILDBkyBH379sWoUaOwaNEixMXFoaSkBJs2bcKoUaPQs2fPNtu1Z88e/Pjjjxg2bBj8/PywZ88eVFRUICEh4ZLPkTkajcZsGVOjaflhCw4OhlqtxgMPPGBUxjQEeiIiIkP/vfvUpfffHeGXUg2a9AIDAlRWFdjaHdYeeOABPPPMM3BycsKbb76JRYsW4emnn8aAAQPw6quvYsqUKR3SoLlz5+LPP//Eiy++CFdXVyxZsgTDhw832uZwVRNwmR902i1jcfbkcWx+awGamxqQMvR2XHfrBJzI2t+u/XuNnoxjf/6G+2/ujyZNHbZt24ZBgwbh+++/x2OPPYZevXpBpVJhzJgxePPNN80ew9/fHz/99BMGDRqESZMm4V//+hc2bdqEZ599FtOmTUNFRQX8/f0xYMAAk7DXGjc3N2zfvh1Lly5FdXU1wsLCsGTJEtx8883tPjcG5eXlJqNlhw8fhl6vh42NDRISEqBWqzF27FjpbsyrOTExERFZp12n6i0W1M5vg71Sgb7+Kou241LI6nFT4eHhmD17ttHdphfKq2rE1wU1ra6/HB8/MhYu3n4Yv3BF2xtfYHSEq9XWwPV6vdkyZmlpKQDAxcXF5NmYSUlJLGMSEdEl64z++0pYU/99WfOsWUq9Vo/NRa1PNNseTfUa7Fm3GrF9B0OhtEHmd18jf88vuO/dry7reJuLahHiYif7u0zq6+uRlZVlFMoyMzOlSXODgoKgVqsxbdo0KZhFRkayjElERFesI/rvjmYt/TdgZWFtS/H/7hq5XAqFAod3/IBtH70JbVMTfMOjMOn1lYi+fiAA4IX01q9dm7r8C0T0MJ5apEEnsLW4FreFu11RuzpSRUWFyWhZbm4u9Ho9lEqlVMa84447pDKmr6+vpZtNRERdVEf03x1Njv13a2RVBr2YqzV8erroWKvr3P0CYOfoZHbdmEjXqz7xnl6vx7Fjx4xC2f79+1FSUgIAcHZ2RmpqqlEZs3v37nByMv8eiIiIOprcyp8XskT/famsYmRNCIHtpRqjWY07i09o5CXvowCwvUSDaDf7Tru7pKGhwWwZ0/D80cDAQKjVakydOlUKZlFRUSxjEhGRxVzN/vtyXI3+uyNYRVgr0WhxusF0Mlq5EAAqGnQo0WgR5GzX5vZtOX36NDIzM40ew5SbmwudTgelUom4uDio1WrcfvvtUhnTz8/vyt8IERFRB7rW+u/OYhVhbV9FvWxTuYECLe28lA9br9ejoKDA5Pqy4uJiAC2P7EpNTcWAAQMwa9YsqYypUlnP7cZERHTt6qr999Um+7Cm0eqRU9kk6w8aaPlGzKlswo3BeqjM3FnS2NiI7Oxso2vLMjMzUVPTUsf39/dHWloaJk+ebFTGtLGxucrvhIiI6Mp1lf5bDmQf1o7XNKPtp4fKgx5AUU0zfPW1UhnT8JWTkwOtVguFQiGVMUeOHCmVMf39/S3dfCIiog5jjf13vKc8bzSQfVgr02ihBKziAxc6HZ5fsgxfvjwfQEsZMyUlBenp6Xj00UelMqbh+aFERERdlTX130q0tJdh7TKVaqwnmUOpRHK/GzH688+hVqsRExPDMiYREV2TrKn/1qPlZgi5knVYE0KgTMYn70IKhQLOAWEYPzxN1rcAExERdSZr67+BlpE1IYQs+295Xkn3F41WoMlaYvlfmvQCGq3cL6ckIiLqPOy/O5asw1qzXp4nrS1a63goBBERUadg/92xZB3WrPSzhs7K/pogIiLqSOy/O5asw5pSfmXjdrGR9VklIiLqXOy/O5ZMm9XCzko/bVsZXpxIRETUke644w6Ehobi0UcfxcaNG6HRaKR17L87lqzDmspWAXtZt9CUvVIBla08P2wiIqKO0tjYiBMnTuCDDz7AyJEj4eHhgYEDB2LGjBmoPl3O/rsDyXrqDoVCAX+VLYpqref2X3+VrSxv+yUiIuooer0e4eHhsLGxgVbb0kc3Nzdj+/bt2L59O5qbmzHwiUXsvzuIrMMaAASo7FBcq7WKifWUAAJVsj+lRERE7dbQ0IDs7Gzs379feoRiZmYmamtrjbZTKBQQQmDmzJl488038eupRvbfHUS+LfuLv8rWKj5ooGUGZH8Zf9hEREQXc+bMGaPnWhueba3T6aBUKqVnW992223w9fXFtGnTAAC2trZwdXXF2rVrMWTIEACAv0rH/ruDyLdlfwlztbOqZ4uFutpZuhlEREQXpdfrUVBQYBLMiouLAbQ82zo1NRX9+/fHzJkzpWdbq1Qqo2PMmDEDGo0GvXv3xpdffonAwEBpPfvvjiP7sKayVSLB0x6HKpsg52lbFAASPO2hsrWyKyqJiKhLa2xsRHZ2tlEoy8zMRHV1NQDA398farUad999N9RqNdRqNaKjo9t8trVSqcSsWbNga2uLF154Aba2xpGC/XfHUQgh0+l6z3Oyrhlr8s5ZuhltmhzrjiBn+SZzIiLq2s6ePWu2jKnVaqFQKKQypuErNTUV/v7+ndYe9t8dQ/Yja0DLRX++jjY43aCTZTpXAPBxtJH1xYlERNR1CCFQWFhoFMr279+PEydOAACcnJyQkpKCG264AdOnT4darUZycjKcnZ2vajvZf3cMqxhZA4Aj5xqx7liNpZvRqjGRrohxd7B0M4iIqItpbGzEoUOHTEbMDGVMPz8/pKWlGY2YxcTEtFnGvFrYf185qwlrAPBNYTVyZVb7NtS6bwt3s3RTiIjIylVWViIzM1MaKcvIyMChQ4ekMmZMTAzUarVROOvMMmZHMdd/P93DF3cvWY2kwbegsqQIi0deh5mf/4TAuOSr0qYvX5gBu4Za7N7ybYcdc9WqVZg9ezaqqqo67JiAlZRBDYYFu6CwuhL1OvnENUcbBYYGu1i6GUREZEWEEDh+/LjJaNnx48cBAI6OjkhJSUGfPn3w8MMPS2VMF5dL62/Ky8vx3HPPYfPmzTh16hQ8PT2RmpqKBQsWoG/fvp3x1sxqq/927xaEZ7ZkQeXhfdXaZKNQwNepY0cfx48fj1tuuUX694IFC7BhwwZkZGRc0XGtKqw52Spxc6gLvi6Qz3DqzaEucJLxHSRERGRZTU1NyMnJMZpUNiMjA+fOtVx47+vri7S0NIwfP96ojHnh3ZWXY8yYMWhubsbq1asRGRmJU6dO4ccff8TZs2ev+NiXoq3+W2ljA1efble1TcHOttBrOvaJBU5OTnBycurQYwIyfzaoObEeDhgQoGp7w6tgYIAKsR7yrnMTEdHVU1VVhV9++QVvv/027r33XqSlpcHFxQVqtRr33nsvNm3aBD8/P8yfPx8bN27EyZMncerUKXz//fdYtGgR7rrrLiQkJHRIUKuqqsKOHTuwaNEiDB48GGFhYejduzeefvppjBgxAgDw5ptvSjcehISEYPr06UZPJli1ahU8PDzw7bffIi4uDiqVCmPHjkVdXR1Wr16N8PBweHp6YubMmdDpdNJ+4eHheOmllzBx4kS4uLggMDAQ36/5oNX+u7KkCE/38EXJ4YMAgGN/7MTTPXyRv2c73pk0BM/fEIp3p96CisJ8o/1++mgJFt6YgBf6hWPdP2bju2X/wLIJg9o8NwMDVHCzNx5Va2xsxKxZs+Dn5wdHR0f069cPe/fuNdrmP//5D2JiYuDk5ITBgwdj9erVUCgUUtnTcL4M///iiy8iMzMTCoUCCoUCq1atAtAy4hYaGgoHh/ZlCKsaWTPo280JzXqBXafqLdqGPt06Pj0TEZH8CSFQVFRkUsYsLCwEADg4OCA5ORm9evXCAw88IJUxXV1dr1obXVxc4OLigg0bNqBPnz5mg4FSqcSyZcsQHh6OgoICTJ8+HfPnz8eKFSukbTQaDZYtW4YvvvgCNTU1GD16NEaPHg0PDw9s2rQJx44dw5gxY9CvXz+MHz9e2u/111/HM888gwULFuD777/H448/jk1xcejbPb3d/feW/3sFt8x5Ec6ePtjw8hNY9+IsPLxyEwBg/6avsO3jpbj9qUUIU1+PA9+vx6//XAGvwNCLHtPQf79/wfL58+dj3bp1WL16NcLCwrB48WIMHz4c+fn58PLyQmFhIcaOHYvHHnsM999/P/bv348nnnii1dcZP348srKy8N133+GHH34AALi7u+Orr77CW2+9hS+++AJJSUntOg9WGdYUCgUGBKhgr1Tgl1LNVX/9gQEq9PWXx+geERF1rubmZuTk5JgEs8rKSgCAt7c30tLSMHbsWKmMGRcX1yGjY1fC1tYWq1atwgMPPID33nsPPXr0wMCBAzFhwgSkpKQAAGbPni1tHxERgZdeegmPPPKIUVhrbm7Gu+++i6ioKADA2LFjsWbNGpw6dQouLi5ITEzE4MGDsW3bNqOwlp6ejqeeegoAEBsbi507d2Lp0qXYuHEo7JXtKz8Oe/QZRF6XDgAYeO8srJ41Ec2NDbBzcMSuLz5Cz9snouftEwEANz74BI7s3oYmTV2rx2ut/66rq8O7776LVatW4eabbwYAfPjhh9i6dSs+/vhjzJs3D++99x7i4uLw+uuvAwDi4uKQlZWFl19+2exrOTk5wcXFBba2tkY3gRQVFcHf3x9DhgyBnV375nazyrAGtAS2vv4qeDvaYHNRLRp0olPvElWg5WaCm0NdWPokIuqizp07J92NafjKzs5GU1MTACA6OhpqtRpz586VgllgYCAUio699qmjjBkzBiNGjMCvv/6KXbt24bvvvsPixYvx0UcfYerUqdi2bRteeeUVHDp0CNXV1dBqtWhoaEBdXZ00J5tKpZKCGgB069YN4eHhRjc7dOvWDeXl5UavfeENDH379sXSpUul/hsA7JUKXOzM+ccmSv/v9tc1bXVnT8MjIBgVx/PRZ9y9RtsHJ/XAsb2/Gi1rT/999OhRNDc3Iz09XVpmZ2eH3r17IycnBwBw+PBh9OrVy2i/3r17X6T15t15551YunQpIiMjcdNNN+HDDz9scx+rDWsGsR4OCHGxw5biWuRUNkEBdGhoMxwvwdMeQ4N5MwERUVcghEBxcbHRhLIZGRkoKCgA0FLG7N69O3r06IFp06ZBrVYjJSUFbm7WN02To6Mjhg4diqFDh+L555/H/fffjxdeeAGDBw/GLbfcgocffhgvvfQSvLy8sGPHDtx3331obm6W9r9w9EehUJhdpte3/RTQC0PtsBBnOHra47cS89vb2J73On/tK8T5r3NB1DtvNrJL6b8Ns5hd2D4hhLTs/P+/cL9LERISgsOHD2Pr1q1SebQtVh/WgJa7TG4Pd0OCRyN+LdWgokF3xaHNsL+Pow0GBKpkP2EeERGZ19zcjNzcXJMypuGOSC8vL6SlpWH06NFGZcz2lqisTWJiIjZs2IA//vgDWq0WS5YsgVLZEmTWrl3bYa+ze/duk3/Hx8cbLXOwaem/XaudsfivZe0do/QNi0Zx9j70GDlOWnbyUIb0/5fSf0dHR8Pe3h47duzAxIktZdXm5mb88ccfUqk4Pj4emzZtMtrvjz/+uOhx7e3tjW68MHBycsJtt92G2267rc22AV0krBnEejggxt0eJRot9lXUI6eyCXq03PLadt7/33ZKBZDo4YAevo4IUNnKdnibiIiMVVdX48CBA0bTZGRlZUllzMjISKjVasyePVuaXDYoKKhL/p4/c+YM7rzzTkybNg0pKSlwdXXFH3/8gcWLF+P2229HVFQUtFotli9fjltvvRU7d+7Ee++912Gvv3PnTixevBijRo3C1q1b8eWXX2Ljxo1mt410awlUI8Nc0Ohpj2N/Lb9YLavvhPuxfuEcBCWqEZHaCxlbNqA0/xCCQiMwJdb9kvpvZ2dnPPLII5g3bx68vLwQGhqKxYsXQ6PR4L777gMAPPTQQ3jzzTfx5JNP4r777kNGRoZ0d2drr2O4cSMjIwPBwcFwdXXF559/Dp1Oh+uvvx4qlQqRkZFttq9LhTWg5YQFOdshyNkONwbrUVTTjFKNFqUaLco0zWgyk9rslYC/yg6BKlv4q2wR6moHFcudRESyJYTAyZMnTUbLjh49CqBlRKN79+5Qq9WYOnWqVMZ0d3e3cMuvHhcXF1x//fV46623pGuyQkJC8MADD+CZZ56Bk5MT3nzzTSxatAhPP/00BgwYgFdffRVTpkzpkNefO3cu/vzzT7z44otwdXXFkiVLMHz48Ivu4+dkB3W4G+wiXfEBgB4+jqixs0OZptlk27RbxqK65Di+X7oA2qZG3DxqDKZNnYr9f+xF4GU8lP21116DXq/H5MmTUVNTg549e+L777+Hp6cngJYbML766ivMnTsXb7/9Nvr27Ytnn30WjzzySKtTcIwZMwZff/01Bg8ejKqqKqxcuRIeHh547bXXMGfOHOh0OqOpUlpjVY+bulJCCGi0AlohoNMDNkrAVqGAylbRJf+qIiLqCrRardky5pkzZwAAnp6eUvnS8Bim+Pj4LlvGtAbh4eGYPXu20d2mV6K9/ffQoUPh7++PNWvWdMjrtuXll1/Ge++9hxMnTnTq63S5kbWLUSgUcLZjKCMikquamhocOHDA6ML/rKwsNDY2AmgZ3VCr1Zg1a5YU0EJCQvgHdxdnrv/WaDR4a/l7GD58OGxsbPD555/jhx9+wNatWzutHStWrECvXr3g7e2NnTt34vXXX8eMGTM67fUMrqmwRkRE8iCEQElJicloWX5+ywz1dnZ2SEpKQlpaGqZMmSKVMQ2zwxMpFAps2rQJCxcuRGNjI+Li4rBu3ToMGTIEAC76HNXNmzejf//+l/yaR44cwcKFC3H27FmEhoZi7ty5ePrppy/7PbTXNVUGJSKiq0+r1SIvL8/k2ZinT58GAHh4eEijZIavhIQE2NvbW7jlZM0Mwd+coKCgTnmGZ2dhWCMiog5TW1trVMbMyMjAwYMH0dDQAKDlWqYLg1loaCjLmEQXwbBGRESXTAiB0tJSs2VMIQRsbW2RlJRkFMpSU1OlO+uIqP0Y1oiI6KJ0Oh3y8vJMgpnh8ULu7u5my5itTWdARJeGYY2IiCS1tbU4ePCgSRmzvr4eABAaGmoUytLS0hAWFsYyJlEnYlgjIrpGlZWVGYWy/fv348iRI1IZMzEx0aSM6eXlZelmE11zGNaIiLo4nU6HI0eOmJQxT506BQBwdXU1KWMmJSWxjEkkEwxrRERdiEajkcqYhqkyDhw4IJUxQ0JCTIJZeHi49CBvIpIfhjUiIit16tQpk9GyvLw86PV62NjYICEhwegRTKmpqfD29rZ0s4noEjGsERHJnE6nw9GjR00mlS0rKwPQUsZMTU01KWM6OjpauOVE1BEY1oiIZESj0SArK8solB04cAB1dXUAWmZeN4yUGb4iIiJYxiTqwhjWiIgspLy83KSMefjwYamMGR8fb3I3pq+vr6WbTURXGcMaEVEn0+v1OHr0qEkwKykpAdDywGlzZUxrenYhEXUehjUiog5UX19vUsbMzMyUypiBgYEmd2NGRUWxjElErWJYIyK6TBUVFcjMzDS68D83Nxd6vR5KpdJsGdPPz8/SzSYiK8OwRkTUBr1ej2PHjpmUMU+ePAkAUKlURmXMtLQ0JCUlQaVSWbjlRNQVMKwREZ2noaHBbBmztrYWABAQEGC2jGljY2PhlhNRV8WwRkTXrNOnTyMzM9MomOXk5ECn00GhUCAuLs5kUtlu3bpZutlEdI1hWCOiLk+v16OgoMCkjFlcXAygpYyZkpJiNFrWvXt3ODs7W7jlREQMa0TUxTQ2NiI7O1sKZPv370dmZiZqamoAAH5+fkhLSzOaWDY6OpplTCKSLYY1IrJaZ86cMVvG1Gq1UCgUiI2NNbm+zN/f39LNJiK6JAxrRCR7QggUFhZKI2WGYHbixAkAgKOjI1JSUpCWlobU1FSkpaUhOTmZZUwi6hIY1ohIVhobG3Ho0CGT68uqq6sBAL6+vibPxoyJiYGtra2FW05E1DkY1ojIYiorK6UypmHE7NChQ1IZMyYmxmwZU6FQWLrpRERXDcMaEXU6IQSOHz9uMlp2/PhxAC1lzOTkZKNQlpKSAhcXFwu3nIjI8hjWiKhDNTU1IScnx+jasoyMDJw7dw4A4OPjY1LGjI2NZRmTiKgVDGtEdNmqqqpM7sbMzs5Gc3MzACA6OtroEUxqtRoBAQEsYxIRXQKGNSJqkxACRUVFJmXMwsJCAICDg4PZMqarq6tlG05E1AUwrBGRkebmZuTk5JgEs8rKSgCAt7e30UiZWq1GXFwcy5hERJ2EYY3oGnbu3DmzZcympiYAQFRUlMndmEFBQSxjEhFdRQxrRNcAIQSKi4uNHsGUkZGBgoICAIC9vb3ZMqabm5uFW05ERAxrRF1Mc3MzcnNzTcqYZ8+eBQB4eXmZjJbFx8fDzs7Owi0nIiJzGNaIrFh1dTUOHDhgNGKWlZUllTEjIyNNgllwcDDLmEREVoRhjcgKCCFw8uRJk9Gyo0ePAgDs7OzQvXt3o1CWmpoKd3d3C7eciIiuFMMakcxotVocPnzY5KHlZ86cAQB4enqaLWPa29tbuOVERNQZGNaILKimpsaojJmRkYGDBw+isbERABAeHm4yqWxISAjLmERE1xCGNaKrQAiB0tJSk0cw5efnA2gpYyYlJZmUMT08PCzbcCIisjiGNaIOptVqkZeXZ3J9WUVFBQDAw8PDpIyZkJDAMiYREZnFsEZ0BWpra82WMRsaGgAAYWFhJsEsLCyMZUwiImo3hjWidhBCoKyszGS07MiRIxBCwNbW1mwZ09PT09JNJyIiK8ewRnQBnU5ntoxZXl4OAHBzczO56D8hIQEODg4WbjkREXVFDGt0Taurq8PBgweNQtmBAwdQX18PAAgNDTUpY4aHh7OMSUREVw3DWisKCwsRERGB/fv3Q61WW7o51AHMlTHz8vKkMmZCQoI0UmYoY3p5eVm62UREdI3r1LBWVlaGV199FRs3bkRxcTHc3d0RExODu+++G1OmTIFKpWrzGAsWLMCGDRuQkZFhtDw8PByzZ8/G7NmzO6XtOp0OFRUV8PHxga2tLX7++WcMHjwYlZWVHTKdwoIFC/Diiy8CAGxsbODh4YHExESMHj0ajzzyCEtqV0Cn0yE/P98olO3fvx+nTp0CALi6upqMliUmJsLR0dHCLSciIjJl21kHPnbsGNLT0+Hh4YFXXnkFycnJ0pQGn3zyCQIDA3Hbbbd11subJYSARivQrBfQC0CpAOyUCqhsFSZlLRsbG/j7+3dqe5KSkvDDDz9Ar9fjzJkz+Pnnn7Fw4UKsWbMGP//8M1xdXTv19bsCjUZjtoyp0WgAACEhIVCr1XjwwQeNyphKpdLCLSciova6lP67K+q0sDZ9+nTY2trijz/+gLOzs7Q8OTkZY8aMgWFA79y5c5g3bx42bNiAhoYG9OzZE2+99RZSU1OxatUqafTJ8GGsXLkSU6dObfP13333Xbz+xhs4ceIE/EPCMfLhOYgZNhZN+pb15QVH8PVLs3HyUCa8g8Mw7blFePXeO7BszVrcN2EMyouLpDKoh4cHBg8eDADS3X333HMPVq1aha+++govvvgi8vPzoVKpkJaWhm+++cboPbfG1tZWCoSBgYFITk7G0KFDkZqaikWLFmHhwoXtO9nXiPLycqORMkMZU6/Xw8bGBgkJCVCr1Rg7dqxUxvTx8bF0s4mI6BJptHocr2lGmUaLUk3Lfw399/nslYC/yhYBKjv4q2wR5moHlW3X+2O8U8LamTNnsGXLFrzyyiuthhaFQgEhBEaMGAEvLy9s2rQJ7u7ueP/993HjjTciLy8P48ePR1ZWFr777jv88MMPANDmg6mFEPj4i68w67HHMHLuQtxx/UAc/nULPnh6Bqa5+yOqVz/o9Xr8c+4UuPsHY/qn36GxrhZrlrwAAPi9vB71B8/Cq65GOl5ISAjWrVuHMWPG4PDhw3Bzc4OTkxNKS0tx1113YfHixbjjjjtQU1ODX3/9FVdSWY6Pj8fNN9+Mr7/++poNa3q93qSMmZGRgdLSUgCAi4sL1Go1hgwZgieeeAJqtRpJSUksYxIRWTEhBEo0WuyrqEdOZRP0AJQAzGQ0SZMeKKrVorhWK22f4GmPHr5OCFTZdplRt04Ja/n5+RBCIC4uzmi5j4+PNFnoo48+iuHDh+PgwYMoLy+XrtF64403sGHDBnz11Vd48MEH4eLiYjQCdTF5VY3YXqrBwkWvo8fICbh+3DQAgG/YIyg6+Ad+XfN/iOrVD0d2bcOZ4kI88MEGuPp0AwAMf/QZfPzIWAAt3xhHzjUDADYU1MAlUitdaO7n5ydds3b06FFotVqMHj0aYWFhAFpGDq9UfHw8tmzZcsXHsQb19fVmy5h1dXUAgKCgIKjVakybNk26+D8iIoJlTCKiLsTQf59u0EEBwDDkcbGgdj79ef89VNmE7Mom+DraYECgCjHu1n8NeKeVQQGYJNrff/8der0ekyZNQmNjI/7880/U1tbC29vbaLv6+nocPXq03a9Tr9VjS3EtciqboEBLibPX6ClG24Sl9sbOzz8EAJw+ng/3bkFSUAOA4KQeZo9d1aTDumM10JbVmaxLTU3FjTfeiOTkZAwfPhzDhg3D2LFjr3giVCFEl/lr4HwVFRUmo2W5ublSGTM+Ph5qtRqjR4+Wypi+vr6WbjYREXWSC/tv4H9B7XIZ9j/d0NJ/J3g2YliwC5ysuDzaKWEtOjoaCoUCubm5RssjIyMBAE5OTgBayl0BAQH4+eefTY7R3jsu86oasbmoFg26lo/H8CFdGHbOD0CXEoYMxyuq0QIA8s81oudfTbOxscHWrVvx22+/YcuWLVi+fDmeffZZ7NmzBxEREe06vjk5OTlXtL+l6fV6HDt2zOjasoyMDJSUlAAAnJ2dkZqaisGDB+Pxxx+XypiG7wsiIur6Wuu/O4rheLmVTSisrsTNoS6I9bDOUbZOCWve3t4YOnQo3nnnHcycObPV69Z69OiBsrIy2NraIjw83Ow29vb20Ol0ZtcVVDfh64Iak+V+ETEo3L8HPUaOl5YVHdgL3/AYAIBveAyqyopRc6Ycrt5+AIDi7P0XfU82dnYAgG+OnUOzgyv6dHOCQtFyF0p6ejrS09Px/PPPIywsDOvXr8ecOXMuerzW5Obm4rvvvsPTTz99WftfbQ0NDcjKyjIaLcvMzERtbS2Alhsn1Go1pk6dKt2NGRUVxTImEdE1SgiBXafqsb1Uc3VeD0C9TuDrghoMDNBJ/bc16bQy6IoVK5Ceno6ePXtiwYIFSElJgVKpxN69e5Gbm4vrrrsOQ4YMQd++fTFq1CgsWrQIcXFxKCkpwaZNmzBq1Cj07NkT4eHhKCgoQEZGBoKDg+Hq6gp7e3s06AT25xdBHD5o9Loe/sHoP2UGPn/yfgQlpCCqd3/kbN+C7J824r531wEAYvoMgndwOL58fgZufuwFNGpqseX/XgFgOiInHTcgpGW08NctEGIIqmo84XmmAD/99BOGDRsGPz8/7NmzBxUVFUhISGjXOdJqtSgrKzOZukOtVmPevHlXcPY7x+nTp82WMXU6HZRKpVTGvP3226Uypp+fn6WbTUREMiGEwC+lGuw+VW+R1/+lVIMmvcCAAJVVBbZOnRS3tLQUr7zyijQproODAxITE3HnnXdi+vTpUKlUqKmpwbPPPot169ahoqIC/v7+GDBgAF599VWEhISgsbERkyZNwo8//oiqqiqsXLkSsTeNw4ge8agqPWHymmMXLMN1t92F3V+uxK9rVuBc2Ul4BoVi8H1z0GPkOGk7w9QdxdkZ8AoKw82zF+DT2ZNw7zv/RuwNf0NlSREWj7wOMz//CYFxLTcN/PjhEuz58hPUnqlA2shxeOKJ+Vj9yjPYt28fqqurERYWhpkzZ2LGjBltnpsLJ8V1d3dHYmIixowZY/FJcfV6vRSQz/8qLi4GAKhUKqSmphpNKtu9e/d2TXJMRETXrt/KNFdtRO1iBgao0Nffevosq3vcVF5Vo9nS55UqzNiD96eNxBPf/A7vkPZfLzY6wtVqa+BASxkzOzvbpIxZU9NyjgMCAkxm+4+KioKNjY2FW05ERNaks/rvy2VN/Xen3g3a0eq1emwuqu2QY2X/tBH2Kmf4hEbizIkC/Pf1ZxGm7n1JQQ0ANhfVIsTFziruMjlz5gwyMzONJpbNycmBTqeDQqFAXFwc1Go1Ro4cKQWzbt26tX1gIiKii+jI/rujWFP/bVVhbUvx/+4auVKNmlpsfvtFnDtVApWHF6KvH4BbHv/HJR+nQSewtbgWt4W7GS13cXFpdZ/Nmzejf//+l/xa7SWEMFvGPHGipWysUqmQkpKCfv36YcaMGVIZsz1PXSAiIrpUHdl/d5TW+m85spoyqNyGTy80JtLVaOK9/Pz8VrcNCgrqsGkqGhsbcejQIaPRsszMTFRXVwNomcTXMJms4b/R0dEsYxIR0VVhbf23HFnFyJoQAttLNUazGsuJAsD2Eg2i3eylu0uio6M7/HXOnj1rVMbMyMjAoUOHoNVqoVAoEBsbC7VajVtuuUUqY3b2w+iJiIhaY439txxZRVgr0WhxusH8XGtyIABUNOhQotEiyNnuyo8nBI4fP27y0PKioiIAgKOjI1JSUtC3b1888sgjUKvVSE5OZhmTiIhk5VrrvzuLVYS1fRX1sk3lBgq0tPNSP+ympiajMqbh69y5cwAAX19fpKWlYcKECdJoWUxMDGxtreKjIyKia1hX7r+vJtn3+BqtHjmVTbL+oIGWb8ScyibcGKyHqpU7S6qqqpCZmWn0CKZDhw6hubkZCoUCMTExUKvVePLJJ43KmHIemiUiIjKnK/Xflib7sHa8phl6SzeinfQAimqaEedhj6KiIpPRssLCQgAtZczk5GT07t0bDz74INRqNVJSUi56BykREZE1scb+O95TnjcayD6slWm0UALW8YHr9Xjz4zX48uV5qKqqAgD4+PggLS0Nd955pzRaFhsbyzImERF1adbUfyvR0l6GtctUqrGeZC4UCvhEJeCJJ56QpsoICAhgGZOIiK451tR/69FyM4RcyTqsCSFQJuOTdyGFQgGfyHg8PuoGBjQiIrpmWVv/DbSMrAkhZNl/y/NKur9otAJN1hLL/9KkF9Bo5X45JRERUedh/92xZB3WmvXyPGlt0VrHQyGIiIg6BfvvjiXrsGalnzV0VvbXBBERUUdi/92xZB3WlPIrG7eLjazPKhERUedi/92xZNqsFnZW+mnbyvDiRCIioo40a9Ys3HjjjXjzzTeRk5MDcV4Jkf13x5J1WFPZKmAv6xaaslcqoLKV54dNRETUUbKysvDTTz9h3rx5SExMREhICB5++GGsXLkStvpm9t8dSNanUqFQwF8l69lFTPirbGV52y8REVFHUqvVsLW1hV7fcqHXyZMn8f7772PatGmYPn06++8OJPszGaCyQ3Gt1iom1lMCCLSyb04iIqK2VFZWIjMzExkZGdLzrbOysqSgdr7BgwfjzTffxL5aW/bfHUS+LfuLv8rWKj5ooGUGZGv7S4KIiMhACIHjx4+bPNv6+PHjAFqebZ2SkoI+ffrg9ttvx0svvQQAsLGxgVKpxIoVK3Dfffe1VMb0jey/O4h8W/aXMFc7q3q2WKirnaWbQURE1Kampibk5ORII2WGr3PnzgEAfH19kZaWhvHjx0vPto6JiZGeba3VarFo0SI0NTUhJCQE69evh1qtlo7P/rvjyD6sqWyVSPC0x6HKJsh52hYFgARPe6hsZX0ZIBERXYOqqqqkMqbhKzs7G83NzQCAmJgYqNVqzJ8/X3q2tb+//0Wv4bK1tcXo0aMBAO+//z7c3NyM1rP/7jgKIWQ6Xe95TtY1Y03eOUs3o02TY90R5CzfZE5ERF2bEAJFRUUmZczCwkIALWXM5ORkaaRMrVYjOTkZrq6undIe9t8dQ/Yja0DLRX++jjY43aCTZTpXAPBxtJH1xYlERNS1NDc3IycnxySYVVZWAgB8fHygVqsxduxYpKWlQa1WIzY2VipjXg3svzuGVYysAcCRc41Yd6zG0s1o1ZhIV8S4O1i6GURE1AWdO3fObBmzqakJABAdHW00WqZWqxEYGCiLqSjYf185qwlrAPBNYTVyZVb7NtS6bwt3a3NbIiKiixFCoLi42OSi/4KCAgCAg4MDunfvLgWytLQ0JCcnm1wvJjdXs/+uLCnC4pHXYebnPyEwLrnV7ayp/7aqsFav1eODQ5Wo18mnyU42CjyY6AknGV+YSERE8tPc3Izc3FyTMubZs2cBAF5eXlL50vAVFxcHO7vOv7aqrKwMr776KjZu3Iji4mK4u7sjJiYGd999N6ZMmQKVStXmMRYsWIANGzYgIyPDqP9eNKIH0ic+iH6THu6Utut1OtRVnobKwxs2trY49sdOfPjgKDz/Sz6cXN2l7a60/y4uLkZkZCQiIyORm5vbUc03S95F2gs42Spxc6gLvi6Qz3DqzaEuDGpERHRR1dXVJmXMrKwsqYwZGRkJtVqNxx9/XApmQUFBFiljHjt2DOnp6fDw8MArr7yC5ORkaLVa5OXl4ZNPPkFgYCBuu+22Szrm1ey/lTY2cPXp1uZ2V9p/r1q1CuPGjcP27duxc+dOpKenX/ax2mJVI2sGv5VpsL1UY+lmYGCACn392/7rgoiIrg1CCJw8edJktOzo0aMAAHt7e6MyplqtRkpKCtzd3ds48tVz0003ITs7G7m5uXB2djZZL4SAQqHAuXPnMG/ePGzYsAENDQ3o2bMn3nrrLaSmpmLVqlW49957jfZbuXIlYm8ahxE94i86srb7y5X4dc0KnCs7Cc+gUAy+bw56jBwnrS8vOIKvX5qNk4cy4RUUhlvnv4KPHxmLu5esRtLgW4zKoE6u7lg88jqj4/e4dTze+eATnNyxCS+++CLy8/OhUqmQlpaGb775xux7NncOoqOjsWLFCmzbtg3l5eX45JNP2nN6L4tVjawZ9O3mhGa9wK5T9RZtQ59uThZ7fSIisiytVmu2jHnmzBkAgKenJ9LS0nD77bdLwSw+Pv6qlDEv15kzZ7Blyxa88sorrYYWhUIBIQRGjBgBLy8vbNq0Ce7u7nj//fdx4403Ii8vD+PHj0dWVha+++47/PDDDwAAd3d3ODo6wsGm9dHC7J824tvXn8WIJxYi+vqByP11C9a9OAvu3QIR1asf9Ho9/jl3Ctz9gzH90+/QWFeLjW+90Orx3LsFYdLrK/HZvHsxZ/1uODq74IYQT4TpqzDgrruwePFi3HHHHaipqcGvv/6K9o5fbdu2DRqNBkOGDEFwcDCuv/56vP322502BYpVhjWFQoEBASrYKxX4xQIjbBxRIyK6ttTU1ODAgQNSINu/fz+ysrLQ2NgIAIiIiIBarcasWbOkYBYSEiKLuzEvRX5+PoQQiIuLM1ru4+ODhoYGAMCjjz6K4cOH4+DBgygvL4eDQ8udlG+88QY2bNiAr776Cg8++CBcXFxga2sLf39/o2M52igQ7WZv9vV/XfN/6HHrBPQdNw0A4Bv2CE4c/AO/rvk/RPXqhyO7tuFMcSEe+GCDVOoc/ugz+PiRsWaPp7SxgcrdEwDg4uWDm2ID0NdfhX379kGr1WL06NEICwsDACQnt34zwoU+/vhjTJgwATY2NkhKSkJ0dDT+/e9/4/7772/3MS6FVYY1oCWw9fVXwdvRBpuLatGgE516l4kCLd9gN4e6INZD3rf4EhHR5RFCoKSkxGS0LD8/HwBgZ2cnlTGnTJkilTE9PDws2/AOdmHI/P3336HX6zFp0iQ0Njbizz//RG1tLby9vY22q6+vl0q+FxPuZo9bIlxN+u/ygiPoNXqK0bZhqb2x8/MPAQCnj+fDvVuQ0TVpwUk92vWebg1zQc+/BlpSU1Nx4403Ijk5GcOHD8ewYcMwduxYeHp6tnmcqqoqfP3119ixY4e07O6778Ynn3zCsNaaWA8HhLjYYUtxLXIqm6AAOjS0GY6X4GmPocG8mYCIqKswXDR/4TQZp0+fBgB4eHhArVZj5MiR0jQZ8fHxsLc3PyrUFURHR0OhUJjc3RgZGQkAcHJqufxHr9cjICAAP//8s8kx2htczfXfgGlQNFwjd+H/t8WwVZhrS9SJPm8uNRsbG2zduhW//fYbtmzZguXLl+PZZ5/Fnj17EBERcdHj/utf/0JDQwOuv/56ozbq9XocOnQIiYmJ7WrfpbD6sAa03GVye7gbEjwa8WupBhUNuisObYb9fRxtMCBQJfsJ84iIqHW1tbVGZcyMjAwcPHhQKu2Fh4dDrVZjxowZUhkzNDTU6sqYV8rb2xtDhw7FO++8g5kzZ7Z63VqPHj1QVlYGW1tbhIeHm93G3t4eOp3uoq93Yf/tFxGD4/v3oMfI8dI2RQf2wjc8BgDgGx6DqrJi1Jwph6u3HwCgOHu/2WN72NtgTKQrKupbRssubItCoUB6ejrS09Px/PPPIywsDOvXr8ecOXMu2uaPP/4Yc+fOxdSpU42Wz5o1C5988gneeOONi+5/ObpEWDOI9XBAjLs9SjRa7KuoR05lE/QAlAD07djfsJ1SASR6OKCHryMCVLbX3A8rEZG1EkKgtLTUbBlTCAFbW1skJSVBrVZj0qRJUhmzPeWva8WKFSuQnp6Onj17YsGCBUhJSYFSqcTevXuRm5uL6667DkOGDEHfvn0xatQoLFq0CHFxcSgpKcGmTZswatQo9OzZE+Hh4SgoKEBGRgaCg4Ph6uoqXd9muGP2fKNCQoCn5uPhKRMRlJCCyN79kbt9C7J/2oj73l0HAIjpMwjeweH48vkZuPmxF9CoqcWW/3sFQEv4UiqAWPeWkc9RES1PJlCFhUGhUODbb7/FLbfcAicnJ2RnZ+PHH3/EsGHD4Ofnhz179qCiogIJCQkXPTcZGRnYt28fPvvsM8THxxutu+uuu/Dss8/i1Vdf7fCbSKxy6o720mj1KKppRqlGi1KNFmWaZjSZSW32SsBfZYdAlS38VbYIdbWDiuVOIiJZ0+l0yMvLMwlm5eXlAFruPrzwEUwJCQlSYKDWlZaW4pVXXpEmxXVwcEBiYiLuvPNOTJ8+HSqVCjU1NXj22Wexbt06VFRUwN/fHwMGDMCrr76KkJAQNDY2YtKkSfjxxx9RVVWFlStXYurUqQgPD8fx48dNXtOw/t1338Xrb7yB4hMn0C0kHCMfnouYYWOk/tswdUdxdga8g8Mw+amXsOShCfjwy/9i4qhbUF5chIiICOzfvx9qtRoA8NJLL2HFihU4deoUpkyZgieffBKPP/449u3bh+rqaoSFhWHmzJmYMWPGRc/LzJkz8dNPPyE7O9tkXUVFBQICArB27VqMHj36ij+D83XpsHYhIQQ0WgGtENDpARslYKtQQGWr4OgZEZGM1dbW4uDBgyZlzPr6limcQkNDTWb7D/trRIWs38X6799++w39+vVDfn4+oqKiLN3UTnFNhTUiIpK/srIyo1C2f/9+HDlyRCpjJiYmGoWy1NRUeHl5WbrZdJWsX78eLi4uiImJQX5+Ph577DF4enoa3Z3Z1XSpa9aIiMh66HQ6HDlyxKSMeerUKQCAm5sb1Go1brrpJjz11FNQq9VITExkGfMaV1NTg/nz5+PEiRPw8fHBkCFDsGTJkg47vouLS6vrNm/ejP79+3fYa7UXR9aIiKjTaTQaqYxpmCrjwIEDUhkzJCTE5PqyiIgIljHpqjPMqWdOUFCQNH3J1cSwRkREHerUqVMmo2V5eXnQ6/WwsbExW8a8cHJVIvofhjUiIrosOp0O+fn5JsGsrKwMAODq6orU1FSjYJaUlARHR0cLt5zIujCsERFRmzQaDbKysowu+j9w4AA0mpbnMwcHB5stYyqVnAaJ6EoxrBERkZHy8nKT0bLDhw9LZcz4+HiTYObj42PpZhN1WQxrRETXKL1ej6NHjxpd9J+RkYHS0lIALXfFmStjWuICa6JrGcMaEdE1oL6+3qiMmZGRgczMTNTV1QEAAgMDpUBmmFw2MjKSZUwiGWBYIyLqYioqKkzKmLm5udDr9VAqlSZlzNTUVPj5+Vm62UTUCoY1IiIrpdfrcezYMZNgdvLkSQCAs7MzUlJSjB7D1L17d5YxiawMwxoRkRVoaGgwW8asra0FAAQEBJhc9B8VFQUbGxsLt5yIrhTDGhGRzJw+fRqZmZlGwSwnJwc6nQ5KpRJxcXEmZcxu3bpZutlE1EkY1oiILESv16OgoMCkjFlcXAwAUKlUSElJMQpmycnJUKlUFm45EV1NDGtERFdBY2MjsrOzjSaVzczMRE1NDQDA39/fpIwZHR3NMiYRMawREXW0M2fOmC1jarVaKBQKxMbGGk2TkZqaCn9/f0s3m4hkimGNiOgyCSFQWFhoMqnsiRMnAABOTk5my5jOzs4WbjkRWROGNSKidmhsbMShQ4dMri+rrq4GAPj6+hpNkZGWloaYmBiWMYnoijGsERFd4OzZsyZlzEOHDkllzJiYGJPry/z9/aFQKCzddCLqghjWiOiaJYTA8ePHTUbLjh8/DgBwdHREcnKy0SOYkpOT4eLiYuGWE9G1hGGNiK4JTU1NZsuY586dAwD4+PgYlTHVajViY2Nha2tr4ZYT0bWOYY2IupyqqiqjMub+/ftx6NAhNDc3AwCio6NNgllAQADLmEQkSwxrRGS1hBAoKioyGS0rLCwEADg4OEhlTMNXSkoKXF1dLdtwIqJLwLBGRFahubkZOTk5JtNkVFVVAQC8vb2lOcsMo2ZxcXEsYxKR1WNYIyLZOXfunMndmNnZ2WhqagIAREVFmdyNGRQUxDImEXVJDGtEZDFCCBQXFxuNlGVkZKCgoAAAYG9vb7aM6ebmZuGWExFdPQxrRHRVNDc3Izc31+T6srNnzwIAvLy8jEJZWloa4uLiYGdnZ+GWExFZFsMaEXW46upqkzJmVlaWVMaMjIw0KWMGBwezjElEZAbDGhFdNiEETp48aTJadvToUQAtZcykpCSjaTJSUlLg7u5u4ZYTEVkPhjUiahetVmu2jHnmzBkAgKenp8loWXx8POzt7S3cciIi68awRkQmampqcODAAaNJZbOystDY2AgAiIiIMAlmISEhLGMSEXUChjWia5gQAiUlJSajZfn5+QAAOzs7JCUlGYWy1NRUeHh4WLbhRETXEIY1omuEVqtFXl6eSTCrqKgAAHh4eJiMliUkJLCMSURkYQxrRF1QbW2tURkzIyMDBw8eRENDAwAgLCzMZJqM0NBQljGJiGSIYY3IigkhUFZWZnRtmaGMKYSAra2t2TKmp6enpZtORETtxLBGZCV0Op3ZMmZ5eTkAwM3NzWikzFDGdHBwsHDLiYjoSjCsEclQXV2d2TJmfX09ACA0NNTk+rLw8HCWMYmIuiCGNSILO7+MafjKy8uTypgJCQlGk8qmpqbCy8vL0s0mIqKrhGGN6CrR6XQ4cuSISTA7deoUAMDV1dVktCwxMRGOjo4WbjkREVkSwxpRJ9BoNDh48KBRKDtw4AA0Gg0AICQkxGwZU6lUWrjlREQkNwxrRFfo1KlTZsuYer0eNjY2SEhIMLkb08fHx9LNJiIiK8GwRtROer0e+fn5JsGstLQUAODi4mIyWpaUlMQyJhERXRGGNSIzNBoNsrKyTMqYdXV1AICgoCCTaTIiIiJYxiQiog7HsEbXvIqKCqNQtn//fhw+fFgqY8bHx5uUMX19fS3dbCIiukZcUVgLDw/H7NmzMXv27A5sElHn0Ov1OHr0qEkZs6SkBADg7OyM1NRUo2kykpKS4OTkZOGWExHRtcy2Iw8m5/CmUCjg4OCAw4cPIywsTFo+atQoeHh4YNWqVZZrXCdSKBRYv349Ro0a1eHHlvPnXV9fj+zsbKNHMB04cAC1tbUAgMDAQKjVakydOlUKZlFRUSxjEhGR7HRoWJOjpqYm2NvbA2gJLs/8/Tn830croReAUgHo9FdWBRZCQKfTwda2y59K2Tp9+rTJaFlubi50Oh2USqVUxhw1apRUxvTz87N0s4mIqJ2EENBoBZr1Quq/7ZQKqGwV18STW9o9jBASEoJZs2ZJF1hfKp1Oh/vuuw8RERFwcnJCXFwc3n77bWn99u3bYWdnh7KyMqP95s6diwEDBkj//u233zBgwAA4OTmZbVN4eDgWLlyIqVOnwt3dHePumYZtJ1vW9xl/Pz7/12dY8M1OfJBTifcOVeJYTTOyzzbgX0eqsO1kHTLLqjF9xkz4+fnB0dER/fr1w969e6Xj//zzz1AoFPj+++/Rs2dPODg44Ndff8WgQYMwc+ZMzJ49G56enujWrRs++OAD1NXV4d5774WrqyuioqKwefPmdp+zX375Bb1794aDgwMCAgLw1FNPQavVGr3XpUuXGu2jVquxYMECaT0A3HHHHVAoFNK/FyxYALVajffffx8hISFQqVS48847UVVVJR1n0KBBJiNmo0aNwtSpU6X1x48fx+OPPw6F4ur8sBjKmOvWrcPf//53jBw5EsHBwfD19cXQoUPxwgsv4NixYxgwYABWrFiBPXv2oKamBtnZ2fjss88wb948DB06lEGNiEjmNFo9ciobse1kHf51pApvHTiD5Vln8d6hSqn/Xp51Fm8dOCP13zmVjdBo9ZZueqdod1j797//jR07dmDGjBmX9UJ6vR7BwcFYu3YtDh06hOeffx7PPPMM1q5dCwAYMGAAIiMjsWbNGmkfrVaLf/7zn7j33nsBAAcPHsTw4cMxevRoHDhwoNU2LX79dSgDo/HQp1sRPWEW9pa3PE8xOKUX4vsPw3fLFxq3DUBRrRZ7y+sxfc4T+GztV5i++F1s3rEH0dHRGD58OM6ePWu0z/z58/Hqq68iJycHKSkpAIDVq1fDx8cHv//+O2bOnIlHHnkEd955J2644Qbs27cPw4cPx+TJk6WJUS/m5MmTuOWWW9CrVy9kZmbi3Xffxccff4yFCxe2ua+BIWSuXLkSpaWlRqEzPz8fa9euxX//+1989913yMjIwKOPPtruY3/99dcIDg7GP/7xD5SWlkrTV3SUhoYG/Pnnn/j4448xc+ZM9O/fHx4eHoiOjsbYsWPxySefQK/XY8qUKfj3v/+Nw4cPo7q6Gr/99htWrFiBBx98EL1794ZKperQdhERUecQQuBkXTP+W1iNdw6exTeFNdhbXo+iWi2aWslgTfr/9d/fFNbgnYNn8d/Capysa0ZXun+y3bW7G264AcuWLcPAgQPx7rvvXvLcUXZ2dnjxxRelf0dEROC3337D2rVrMW7cOADAfffdh5UrV2LevHkAgI0bN0Kj0UjrX3/9dUycOFEa8YmJiTFqU1GDArXNeoRc1w9xdz4Mw8d0/mc8fObfsWz8QBTs24WIHn2N2thQX4c9X67C2BeXwyl1IPYA6DtnMTZv2YqPP/5YahcA/OMf/8DQoUON9k9NTcXf//53AMDTTz+N1157DT4+PnjggQcAAM8//zzeffddHDhwAH369Lno+VqxYgVCQkLwzjvvQKFQID4+HiUlJXjyySfx/PPPt+vaKsMdix4eHvD39zd+rw0NWL16NYKDgwEAy5cvx4gRI7BkyRKTbc3x8vKCjY0NXF1d27X9xZw5cwaZmZnStWUZGRnIycmBTqeDQqFAXFwc1Go1Ro4cKV1f1q1btyt6TSIiko+8qkZsL9XgdIMOCsBs/30x+vP+e6iyCdmVTfB1tMGAQBVi3B06vL1XW7vDmouLC4QQ0Ov1KCgoQEJCwiW/2HvvvYePPvoIx48fR319PZqamqBWq6X1U6dOxd///nfs3r0bffr0wSeffIJx48bB2dkZAPDnn38iPz8fn332mbSPoU0f7TiIWu8I6AQQnJCK1vJ0t8g4pI0Yh++WvYRHVm0yWnfmRCF02maEpfaW9q/SKeETr8b3ew9gxnnDqz179jQ5tmGEDQBsbGzg7e2N5OTk/732XwGjvLy8zXOVk5ODvn37GpUX09PTUVtbi+LiYoSGhrZ5jIsJDQ2VghoA9O3bF3q9HocPH77i8NUaIQQKCgpMri87ceIEAEClUiElJQX9+vXDjBkzoFar0b17d+nzJyKirqVeq8eW4lrkVDbB0Ntd6XiYYf/TDTqsO1aDBM9GDAt2gZOt9d5A1u6wlpGRIf3/5QSFtWvX4vHHH8eSJUvQt29fuLq64vXXX8eePXukbfz8/HDrrbdi5cqViIyMxKZNm/Dzzz9L6/V6PR566CHMmjVLWlZQ3YRfSupwzi0INn8ts3O6eOlryMNPYskdfZC9bdMFa1o+4vMDkgAAIXCuSY8PDlXCq7YJAMwGCDs7O6N/KxQKo2WG4+r1bf+tIIQwuQ7MMKRrWK5UKk2GeZubm9s8tjmGY3bUsRsbG6W7MQ1fmZmZqK6uBtDyWaelpWHixInSVBnR0dGwsbFp48hERNQV5FU1YnNRLRp0LX1NRxctDcfLrWxCYXUlbg51QayHdY6ytTusRUdHX9EL/frrr7jhhhswffp0adnRo0dNtrv//vsxYcIEBAcHIyoqCunp6dK6Hj16IDs7G9HR0RBCYNepevxZo4FL0KW1xcM/CH3H3Yct77wMr+Bwabl3SARs7OxRmLEH6oCWUSddczOKD2UgfeJDqNcJ/Fracv1bZ9fCExMTsW7dOqPQ9ttvv8HV1RVBQS1v2NfX1+haserqahQUFBgdx87ODjqdzuT4RUVFKCkpQWBgIABg165dUCqViI2NNXtsnU6HrKwsDB48WFpmb28PnU6Hs2fPIjMz0yiYHTp0CFqtFgqFArGxsVCr1bjlllukMmZnjd4REZG8Gfrv7aVtX7/dIa8HoF4n8HVBDQYG6NCnm5PV3UHa7rB25MgR5OTkYOvWrVi+fHmr2508edJoFA5oGYmLjo7Gp59+iu+//x4RERFYs2YN9u7di4iICKNthw8fDnd3dyxcuBD/+Mc/jNY9+eST6NOnD6ZPn44eoyYjt16J8oIjyN/9M2578rX2vhUAwKBpj2Hv+jU4W1KElGG3AwDsnZxx/dip2Lx0AZzcPOAREIztq5ejuaEevUZNMtp/Z5kGIzw8Ou0Dnz59OpYuXYqZM2dixowZOHz4MF544QXMmTNHul7tb3/7G1atWoVbb70Vnp6eeO6550xGpsLDw/Hjjz8iPT0dDg4O8PT0BAA4OjrinnvuwRtvvIHq6mrMmjUL48aNk0LU3/72N8yZMwcbN25EVFQU3nrrLVRVVaG2thbr169HRkYGqqqq8Nxzz+GJJ56QjpmSkoK+ffvikUcegVqtRnJyMsuYREQEoCWo/VKqwe5T9RZ5/V9KNWjSCwwIUFlVYGt3WEtLS0NUVBTGjx9/0e3eeOMNvPHGG0bLVq5ciYcffhgZGRkYP348FAoF7rrrLkyfPt1kKgulUompU6filVdewZQpU4zWpaSk4JdffsGjTzyFj1cPBYSAV3CEFLYuhcrdEwOnzsT377xstPymWc9BCD2+fO5RNGpqEZSYinv/by2c3DyMtttb0QBvz3r09e+cuw2DgoKwadMmzJs3D6mpqfDy8sJ9990n3cAAtNzEcOzYMYwcORLu7u546aWXTEbWlixZgjlz5uDDDz9EUFAQCgsLAbSMlI4ePRq33HILzp49i1tuuQUrVqyQ9rv77rvx448/Yvz48dDr9fD394dGo8G6deuwbt06+Pr6IiIiAgUFBdBqtWhubkZNTQ3nmyMiolbtOlVvsaB2fhvslYpO6787gyyfDfrAAw/g1KlT+M9//mOyLq+qEV8X1FigVeaNjnC1uhr4ggULsGHDBmkEtLKy0mwZs7m5GQqFAjExMUbPxjSUMa3prxIiIrIs9t+XT1bDIOfOncPevXvx2Wef4ZtvvjFZX6/VY3NRrQVa1rrNRbUIcbGzirtMhBAoKipCbm4uysrKMGrUKGRkZOD48eMAWsqYycnJ6N27Nx588EGo1WqkpKTAxcXFwi0nIiJrxv77ysgqrN1+++34/fff8dBDD5nMYQYAW4r/d9eIXDToBLYW1+K2cLdL2u/hhx/GP//5T7Pr7r77brz33ntX1K6mpibk5OSYTJNheEqBjY2NNIedYbQsNjaWZUwiIupwXan/tgRZlkHNkdvw6YXGRLpe0sR75eXl0jQWF3Jzc7ukRyKdO3fOqIy5f/9+ZGdnS1NtREdHS4HMME1GQEAAy5hERNTpulr/bQlWEdaEEPg4twpnGnQdPg9LR1AA8HG0wbT4zrs7FGg5DydOnDAZLTPcVODg4IDk5GSja8tSUlLg6uraaW0iIiJqDfvvjmEVNa8SjRanG0znCpMLAaCiQYcSjRZBznZtbt8ezc3NyM3NNXoEU0ZGBiorKwEA3t7eUKvVGDNmjBTM4uLiWMYkIiLZuBb7785gFT37vop6o2eFyZECLe28nA/73LlzOHDggFEoy8rKQlNTy9MSoqKioFarMWfOHCmYBQUFyfqvACIioq7ef18tsg9rGq0eOZVNsv6ggZZvxJzKJtwYrIeqlTtLhBAoLi42KWMeO3YMQMsTAZKTk5GWloZ7771XKmO6ucn/4kciIqLzdaX+29JkH9aO1zSj7SdpyoMeQFFNM+I9HdDc3IzDhw+bBLMzZ84AALz+n707j4uqbP84/hn2XUBQQET2fRnc0iwztTLN8qc9ZpuZbWa2PpmVpWWLZWZaZrvaYrbanmmZZmVZJvsiiiIuoKKAIMswM+f3Bw+T46CiAmcGrvfr5et5PDNzzjUz5P3lvs65j68vWq2WMWPGmGbLYmNjLe4vKoQQQtgiWx2/rZHVh7XSGj12YBtfuGJk2effsO7158jOzqa+vh6A8PBwtFot9957rymYBQcHSxtTCCFEh2VL47cdjfVKWDtLJTW2k8wVNDS4eZOcnMzEiRPRarWkpKTQpUsXtUsTQggh2pUtjd9GGi+GsFZWHdYURaHUij+8E2k0GoITUnnx2mEyayaEEKLTsrXxGxpn1hRFscrx2zrPpPufGr2CzlZi+f/ojAo1ems/nVIIIYRoOzJ+ty6rDmsNRuv80E5Hb/3rDAshhBBtRsbv1mXVYc1Gv2sMNvbbhBBCCNGaZPxuXVYd1uysr23cIvZW/akKIYQQbUvG79Zl1RcYONrot+1ghScnCiGEEK1p/vz57Nixg5EjRzJ06FA8PDxMj8n43bqsOqy5OWhwssOmTlJ0stPg5mCdX7YQQgjRWr788kt+//133njjDRwcHLjgggu44oorGDFiBHFxcTJ+tyIrnfBrpNFoCHCz6jxpIcDNwSov+xVCCCFaU58+fUx33dHr9WzYsIEHH3yQxMRE7r33Xhm/W5HVf5KBbo7srdbbxMJ6dkCQjf1wCiGEEKfTdG/rtLQ00+0Tf/31VxoaGiyeGxYWxgMPPECRk4zfrcV6K/ufADcHm/iioXEFZFv7TUIIIYQ4XkNDA/n5+Rb3tj5y5AgAXbt2RavVMmLECD744AOgsROmKAozZszg6aefxsHBgbryehm/W4n1VvY/vTwdbereYiGeciN2IYQQtuHo0aNkZGSYhbLs7Gx0Oh0AERERaLVa7r//ftO9rXv06IFGo6G+vp6VK1diMBjw9PRkxYoVXHHFFaZ9y/jdeqw+rLk52BHn40RuuQ5rXrZFA8T5OOHmYNWnAQohhOiEFEVh3759FrNlhYWFADg5OZGYmIhWq2XSpElotVqSk5NPeW9rZ2dnBg4cSE1NDZ9//jmhoaFmj8v43XqsPqwB9PZ3Jadcp3YZp6TQWKcQQgihJr1e32wb8/DhwwD4+PiQmprKVVddZZoti42NNV0scCbWr1+Pvb39SU/Ml/G7ddhEWAtyc8DfxZ6yOoNVpnMN4Odib9UnJwohhOh4qqqqyMzMNAtlWVlZ1NfXA40n+2u1Wu655x5SU1PRarUEBwe32lWPDg6nHvdk/G4dGkWx0hthnWB7ZT2f76xSu4yTGhfuSVQXZ7XLEEII0QEpisL+/fstZst27NgBgKOjo6mN2fQnOTkZb29vdQtHxu/WYN1R8jhRXZyJ86kn38p63029bmv/ooUQQtgGvV5PQUGBRTA7dOgQAN7e3mi1WkaPHm3WxnRycjLtY8OGDfj4+FBeXt7iwBYaGsp9993Hfffd16rvR8bvxu/j4osvPqPv43g2E9YALg32oOhoObUG6/m6Xew1XBLscfonCiGEEP8zadIk3n33XSZPnswtt9xiFsq2bt2KwWAAGgOUVqvlrrvuMgWzkJCQdlm8tTXDW2uP34/09sfByZkHVv2BT1BP0/b3H5iIi6cX/3ly8Wn30Z7j9/nnn09JSckpL9g4FZsKa64Odlwe4sGqXdYznXp5iAeuVnwFiRBCCPUpikJpaakpkG3YsAEHBweWLl3K0qVLcXR0JCEhgcTERLKzs/Hy8mLo0KF8+OGHapfeKlpr/NY36HBw/N8MokbDj68/x/g5r57VvpobvxVFwWAwnPZcvDPl5OREQEDAWb/e5lJGtLczgwPd1C4DgIsC3Yj2lvanEEKIfxkMBvLy8li5ciUzZszgsssuIyAggKCgIEaOHMnzzz9PXV0dISEhBAcH88wzz1BdXU1aWhqXX345kZGRDBgwwNTWrK+v55577qFbt264uLhwwQUX8Pfff5sd8/vvvyc6OhpXV1cuvvhiioqKLOratGkTgwcPxtXVlZ49e3LPPfdw7Nixs36Pt9xyC2FhYbi6uhITE8OiRYtMj2/cuBFHR0dKS0tN26K9ncl4Yw5v3DLatG13xl+8cctoHh/Yk+cuT+HreY+gq/23pudH9ebnt1/k09nTeGJwOKueut/02PnX3Er6959Ruj33pHXqdfV8Pe8Rnh4Wx+MDgnl98ij25KSZxu8NGzag0WhYs2YNffv2xdnZmV9//ZUhQ4Zw9913c9999+Hj40P37t158803OXbsGDfffDOenp5ERESwevXqFn1eTcepqKgAYPny5Xh7e7NmzZoWvd7mwhrAwO6uDOyu7mW2A7u7MkDlGoQQQqjr2LFj/PHHH7z22mvccccdnHfeeXh6ehIfH891113Hxx9/jKurK3feeSdffPEFu3btory8nBEjRpCUlMQDDzzAzz//bApmS5cuZfLkyWbHeOihh/j8889599132bp1K5GRkVx22WWmOwrs2bOHsWPHMnLkSNLT07n11lt5+OGHzfaRlZXFZZddxtixY8nMzOTjjz/mt99+Y9q0aWf1vo1GI8HBwXzyySfk5uYya9YsHn30UT755BMABg8eTHh4OO+//77pNXq9np+/+JhrbrwJgNLtuSy9azwJQ0dx78cbuPa5t9idvpmvnzOvfeN7r9I9Io5pH/zE0Fv/a9oektKP2Asv5YdXnj5pnasXPUnOum/5z5xXmPbhOrr2DOP9u68h2rHW7HkPPfQQc+fOJS8vj+TkZADeffdd/Pz8+Ouvv7j77ru58847+c9//sP555/P1q1bueyyy7jxxhupqak5q8+wpqaG+fPnt+i5NtUGbaLRaBgc6IaTnYZfSs7uQzoXFwW6MTDAOmb3hBBCtI/j25hNfwoKClAUBQcHB+Lj49FqtUyYMAGtVktKSgq+vr6n3OeNN97II488QlFRERqNht9//52PPvqIDRs2AI1h8LXXXmP58uVcfvnlALz11lv8+OOPvPPOO0yfPp3XXnuN8PBwXnrpJTQaDTExMWRlZfH888+bjvPCCy9w3XXXmc4/i4qK4uWXX+aiiy7itddew8XF5Yw+C0dHR5588knT38PCwti0aROffPIJ48ePB+CWW25h2bJlTJ8+HYDvvvuOmpoaHrvtBrKq7bjr8VfRjhjHBddPAcAvJILR05/lzduu4qpHX8DRubGmiH4XMnjiXc3Wcdndj/HyNRexa+sfhPUeaPaYrvYYmz9dztVPvkLMoOEALHz1DSYMiGfp0qWmugDmzJnDJZdcYvb6lJQUHnvsMQAeeeQRnnvuOfz8/LjtttsAmDVrFq+99hqZmZkMGDDgjD4/aLyt1+uvv96i59pkWIPGwDYwwI2uLvasLq6mzqC06VUmGhpPRrw8xENan0II0YEZDAa2b99uEcwOHDgAgJeXF1qtlssuu4wZM2ag1WqJj4/H2fnMxwY/Pz9GjRrFu+++i6IojBo1Cj8/P9PjhYWFNDQ0MGjQINM2R0dH+vfvT15eHgB5eXkMGDDA7KKDgQPNg8s///zDjh07WLFihWmboigYjUZ27dpFXFzcGdf++uuv8/bbb7N7925qa2vR6XRotVrT45MmTeKxxx7jzz//ZMCAASxdupTx48fj4eHBQA+oKsxiV2Eh6as/O64mUIxGyvcV0y08GoDguJST1tA9PIbUUeP54eWnuHP592aPHd5ThEHfQGhKf1yPG7+P/+ya9O3b12LfTTNsAPb29nTt2pWkpKR/j929OwAHDx5swadlyc3NjYiIiBY912bDWpNob2d6ejiydm81eeU6NNCqoa1pf3E+TlwSLBcTCCFER1JTU0NWVpZZKMvMzDS1tnr27IlWq+X22283LSobGhraqldjTp482dSOfPVV85Plm5ZCPfF4iqKYtrVkuVSj0cgdd9zBPffcY/FYSEjIGdf8ySefcP/99/Piiy8ycOBAPD09eeGFF9i8ebPpOd26dWP06NEsW7aM8PBwvv/+e9OMIYADCrfffjvJV09mR2WD2fjtHRhsep6j66k7WcOnzODF/xtAzvrvT3ikcW9R3k5cF+9jGr+P/+yauLu7W+z3xDs6aDQas21N+zAaz+7up2dyxwibD2vQeJXJVaFexHnX82tJDYfqDOcc2ppe7+diz+AgN1lHTQghbNyBAweabWMajUbs7e1Nbcz//Oc/pjZm165d27yuESNGmG6cftlll5k9FhkZiZOTE7/99hvXXXcd0Ng+27Jli6mlGR8fz5dffmn2uj///NPs77179yYnJ4fIyMhWqfnXX3/l/PPPZ+rUqaZtTfcZPd6tt97KhAkTCA4OJiIiwmyGsHfv3mzLy2XJkFQKKs5+/PYO6MHA8bewdvEz+AaHmrbHREbi6OSE6+4MXAclAJafna3oEGGtSbS3M1FdnNhfo2froVryynUYabyKoiW5t+l5dhqI93amt78LgW4O7bKejRBCiNZhNBrZsWOHKZClpaWRnp5uujLR09OTlJQUhg8fzvTp001tzDM9b6u12Nvbm9py9vb2Zo+5u7tz5513Mn36dHx9fQkJCWHevHnU1NRwyy23ADBlyhRefPFFHnjgAe644w7++ecfli9fbrafGTNmMGDAAO666y5uu+023N3dycvL48cff+SVV145aW1NN38/XkhICJGRkbz33nusWbOGsLAw3n//ff7++2/CwsLMnnvZZZfRpUsXnn76aebMmXPKmi50c+P37dl8/t0aBt/3DEYaJ05aYujke/n7i/c5sr+YwVeMZWJ0FwLdulJwms/OVnSosAaN05I93B3p4e7IsGAjxVUNlNToKanRU1rTgK6Z1OZkBwFujgS5ORDg5kCIpyNu0u4UQgirV1NTQ3Z2tkUbs2lJiuDgYLRaLbfeeqtpUdmwsDDs7Kzr33gvL6+TPvbcc89hNBq58cYbqaqqom/fvqxZswYfHx+gMTx9/vnn3H///SxZsoT+/fvz7LPPml1VmpyczC+//MLMmTO58MILURSFiIgIrrnmmlPWNX/+fIsrFpctW8aUKVNIT0/nmmuuQaPRcO211zJ16lSLpSzs7OyYNGkSzz77LBMnTjR77FQ1TUvypbiqgYX2Gnyc7XGy49Tjd7cgjt7/IC89NYueHo4EuTu26LOzFTZzb9DWoCgKNXoFvaJgMIK9HThoNLg5aGT2TAghrNyhQ4fMQllaWhrbtm0ztTFjY2PRarWmc8tSUlLMTtYX6rjttts4cOAAX3/99Vnvo7OP350qrAkhhLB+RqORwsJCi/PL9u/fD4CHhwcpKSlmNy1PSEjA1VXWvrQmlZWV/P3331x55ZV89dVXFktjiJaTsCaEEEI1tbW15OTkmJ1blpmZSXV1NQA9evQwC2VarZbw8HCra2MKS0OGDOGvv/7ijjvu4KWXXlK7nDY1ZcoUPvjgg2Yfu+GGG1q8ntrJSFgTQgjRLsrKyixmy/Lz8zEYDNjZ2ZnamE1/UlJS6Natm9plC3FaBw8e5OjRo80+5uXldc4/xxLWhBBCtKqmhVabZsqa/uzbtw9ovMLxxDZmYmKitDGFOAkJa0IIIc5aXV2dqY3Z9CcjI4OqqioAAgMDzUJZamoqERER0sYU4gxIWBNCCNEihw8ftmhj5uXlmdqYMTExFm3MplvyCCHOnoQ1IYQQZhRFYdeuXRbBbM+ePUDjPQ2Tk5PNlslITEzEze3UtwUSQpwdCWtCCNGJ1dfXN9vGbDpZOiAgwOJqzMjISIuV9oUQbUfCmhBCdBJHjhwhIyPDbJmMvLw89Ho9Go2G6Oho00xZUxszICBA7bKF6PQkrAkhRAejKApFRUUWbczi4mIAXF1dTW3Mpj9JSUm4u7urXLkQojkS1oQQwobpdDpyc3PNlsnIyMigsrISgG7dupnNlmm1WqKioqSNKYQNkbAmhBA2ory83NTGbPqTm5tLQ0MDGo2GqKgoi/PLAgMD1S5bCHGOJKwJIYSVURSF4uJii0Vld+/eDYCLi0uzbUwPDw+VKxdCtAUJa0IIoSKdTkdeXp7F+WUVFRUA+Pn5WbQxo6OjcXBwULdwIUS7kbAmhBDtpLKy0iKU5eTk0NDQAHDSNqZGo1G5ciGEmiSsCSFEK1MUhT179lgEs127dgHg7OxMUlKSWShLTk7G09NT5cqFENZIwpoQQpyDhoaGZtuY5eXlAHTt2tWijRkTEyNtTCFEi0lYE0KIFqqsrCQzM9MslGVnZ6PT6QCIiIgwu2G5VqslKChI2phCiHMiYU0IIU6gKAp79+61mC3buXMnAE5OTs22Mb28vFSuXAjREUlYE0J0ag0NDWzbts0UyJqWyzhy5AgAvr6+zbYxHR0dVa5cCNFZSFgTQnQaR48ebbaNWV9fD0B4eLjF1ZjBwcHSxhRCqErCmhCiw1EUhf3795vNlKWnp1NYWAg0tjETExMt2phdunRRuXIhhLAkYU0IYdP0er1ZG7PpT1lZGQA+Pj4Ws2VxcXHSxhRC2AwJa0IIm1FVVWXRxszKyjK1McPCwkhJSTE7x6xnz57SxhRC2DQJa0IIq6MoCiUlJRazZTt27EBRFBwdHUlISDBbJiM5ORlvb2+1SxdCiFYnYU0IoSq9Xk9BQYFFMDt06BAA3t7ezbYxnZycVK5cCCHah4Q1IUS7qa6uJisry2yZjKysLOrq6gDo1auX2YKyWq2WkJAQaWMKITo1CWtCiFanKAqlpaUWs2Xbt29HURQcHBzM2pharZaUlBR8fHzULl0IIayOhDUhxDkxGAxs377dYpmMgwcPAtClS5dm25jOzs4qVy6EELZBwpoQosWOHTtm1sZMT08nMzOT2tpaAEJCQiyCWWhoqLQxhRDiHEhYE0I068CBA2YzZenp6RQUFJjamPHx8RZtTF9fX7XLFkKIDkfCmhCdnMFgYMeOHRbnl5WWlgLg6elpMVuWkJAgbUwhhGgnEtaE6ERqamqabWPW1NQA0LNnz2bbmHZ2dipXLoQQnZeENSE6qIMHD1rMlm3btg2j0Yi9vT1xcXFmy2SkpKTQtWtXtcsWQghxAglrQtg4o9HYbBuzpKQEaGxjpqSkWLQxXVxcVK5cCCFES0hYE8KG1NbWkp2dbbaobGZmJseOHQOgR48eZgvKarVawsLCpI0phBA2TMKaEFbq0KFDFrNl+fn5pjZmbGysxdWY/v7+apcthBCilUlYE0JlRqORnTt3Wiwqu3//fgA8PDyabWO6urqqXLkQQoj2IGFNiHZUV1dn1sZMT08nIyOD6upqAIKCgiyuxoyIiJA2phBCdGIS1oRoI2VlZc22MQ0GA3Z2ds22Mbt166Z22UIIIayMhDUhzpHRaGTXrl0WwWzv3r0AuLm5mbUxU1NTSUhIwM3NTeXKhRBC2AIJa0Kcgbq6OnJycizamFVVVQAEBgY228a0t7dXuXIhhBC2SsKaECdx+PBhMjIyzJbJyMvLw2AwoNFoiImJsVhUtnv37mqXLYQQooORsCY6PUVRmm1j7tmzB2hsYyYnJ5vNliUmJuLu7q5y5UIIIToDCWuiU6mvryc3N9dsmYyMjAyOHj0KQPfu3S0WlY2MjJQ2phBCCNV0iLA2adIk3n33XQAcHBzw9fUlOTmZa6+9lkmTJrVo2YPQ0FB2794NgIuLC927d6d///5MmTKFoUOHtmn9om0cOXLErI2Znp5Obm4uer0ejUZDdHS0xfllAQEBapcthBBCmHFQu4DWMmLECJYtW4bBYODAgQP88MMP3HvvvXz22Wd8/fXXODg4oCgKNXqFBqOCUQE7DTjaaXBz0AAwZ84cbrvtNnQ6HUVFRXzwwQcMHz6cp556ipkzZ6r8DsXJKIrC7t27zRaUTU9Pp7i4GABXV1eSk5MZOHAgd955J1qtlqSkJGljCiGEjTjV+K3RaNQur811mLDm7OxsmhXp0aMHvXv3RtuvP6MuvYSHX3qD3mOup7RGj85o+VonOzimN1JqdKbc2Ydefo6EhIQwePBgAgMDmTVrFldffTUxMTHt/K7EiXQ6namNefyfyspKAPz9/UlNTWXChAmmk/+joqKkjSmEEDakRm9kd1UDpTV6SmoaTjl+B7g5EOjmSICbA708HXFz6HiLiHeYsNZEURT21+jZeqiWPD8tgdEJfPvVF/gNv+akr9EZQW+E4iodXxVVYQfE+TjR29+Ve+65h6eeeoqvvvqKhx56qP3eiKC8vLzZNmZDQwMajYaoqCi0Wi0zZswwa2N2ht+yhBCiozEbv8t1GAE7oJmMZqIzQnG1nr3VetPzm8bvIDeHDjMedKiwVlBRz8aSGsrqDGgABfAPjaJ0e26LXt908p4RyC3XkVOuw9/Fnq7+3SgqKmqbogWKolBcXGwxW9b0mbu4uJCUlET//v254447TG1MDw8PdQsXQgjRKpobv+HUQe14xuP+9/jxe3CQG1FdnFu93vbWIcKawahQUqNn1a4qmjJ00xetKAqcRbJuen1ZnYE6vZGiqgZq9UZcO+D0anvS6XTk5eVZBLOKigoA/Pz8SE1N5T//+Y9ptiw6OhoHhw7xoyqEEOI4tXoja/dWk1eusxi/z9bx4/fnO6uI86nn0mAPmx6/bX4ELKioZ1uljmMNjbn6xC/50K7t+ASFnPX+qyuOcKy8DI1fMG/mlnN5iAfR3raf0ttDZWWlxaKyOTk5NDQ0ABAZGUlqairTp083BbPAwMAOM20thBDi5Aoq6lldXE2doXHkbu2lKZr2l1+uo+iobY/fNhvWFEXhjwO1bCypwXCS1UcK//qV0h25DLr+jrM+zqaVb6KxsyNuyOXUGhRW7ariokADA7q7Sqj4H0VR2LNnj8Vs2a5du4DGiz+SkpLo27cvt956K1qtluTkZDw9PVWuXAghRHs7fvxul+OBzY/fNhnWFEXhl5Ia/jxQa9qm19VTVXYAo9FI9eGDFGz6mQ3LFhF74aX0vuLkFxccr77mGFVlBzDo9ZTv203a95+x5csPuOzux/ALCTc975eSGnRGhcGBbjb3hZ+rhoYG8vPzLZbJKC8vB6Br166kpqYybtw402xZTEyMtDGFEEI0O363J1sdv21yUdxNpTVmifzT2dPY+s3HANg5OODq6U1gdAIpI8bSe/SEFi2K+/yo3lSUNN5eyN7RCc+u3eiZ1Ifzrp5ERL8Lmn3NRYFuDAxwa4V3ZJ0qKyvJzMw0C2XZ2dnodDoAIiIiLBaV7dGjh039ByCEEKL9nDh+q8XWxm+bC2sFFfWs2lWldhkmY8M8bbYH3kRRFPbu3WvRxty5cycATk5OJCUlmYWy5ORkvLy8VK5cCCGErZDx++zZVG+qVm9kdXG12mWYWV1cTU8PR5u5yqShoYFt27ZZBLPDhw8D4Ovri1arZcyYMaZFZWNiYnB0dFS5ciGEELZKxu9zY1Mza18VHSW/XHfGV4ykff8ZXz7z32Yf8w7syf2f/XbWNWloXIDvylDrm2U6evRos23M+vp6AMLDwy3amMHBwdLGFEII0arOdvxuS9Y8fp/IZsLauUyf1h+rpurwwWYfs3dwxCeo57mUBsC4cE/VFt5TFIX9+/ebhbK0tDQKCwuBxjZmQkICqampZm3MLl26qFKvEEKIzsPa2p8nUnP8bimbaIMqisLGkhqzVY3PhLO7B87ubbfavQbYuL+GSC+nNp+V0uv1zbYxy8rKAPDx8UGr1XLllVeagllsbCxOTk5tWpcQQghxonMdv9tae47f58Imwtr+Gj1ldQa1yzgpBThUZ2B/jZ4e7q13bld1dbWpjdm0VEZ2djZ1dXUAhIWFodVqufvuu03BrGfPnlb9AyeEEKLz6Kzjd2uzibC29VCt1abyJhoa6zybL1tRFEpKSixmy3bs2IGiKDg6OpKQkIBWq+WGG25Aq9WSkpKCt7d3q78PIYQQorV09PG7vVh9WKvRG8mzspMSm6MAeeU6hgUbcTvFlSUGg4GCggKLRWUPHToEgLe3N1qtllGjRplmy+Li4qSNKYQQwqZ0tPFbTVYf1nZXNWBUu4gWMgLFVQ3E+jSeqFhdXU1WVpZZKMvKyqK2tnHl5l69eqHVapk6dappmYyQkBBpYwohhLB5tjx+WxurD2ulNXrswCa+cI2i8MXPv5L+yVukp6ezfft2FEXBwcHB1Ma89tprTW1MHx8ftUsWQggh2oQtjd92NNYrYe0sldTYUjJXKK6opbS0lBEjRvDII4+Y2pjOztb5AyCEEEK0BdsavxsvhrBWVh3WFEWh1Io/vBNpNHZE9R3IkskbpJUphBCiQxszZgx79+5lzpw5XH755Wbjnq2N39A4s6YoilWO39Z5Jt3/1OgVdLYSy/9HZ2ysWwghhOjIcnNz+eeffxg1ahR9+vTh+++/p2mdfdscvxWrHb+tematwWidH9rp/P7nZlyUBrXLEEIIIdpM08VyABkZGYwaNYqoqChmzJjBuBsmqVfYOdBb6U2drPp2U0fqDLyZV652GWfsxTHnUVa8U+0yhBBCCFXsOXKUD4rq1S7jjN0e54Ovi73aZViw6pk1O+trG7fIj2vX4m5nY/O/QgghxBm49NJLKSoqAsDe3h6DwcDQoUOZOXMmHm5ugO2FNXsrPTnMqsOao42mtajwMNwdrfQbF0IIIVqBo+O/K/6PHDmSJ598ktTUVACONdjmhIWDFV5cAFYe1twcNDjZYVMnKTpqGusWQgghOrIhQ4YQFxfH7Nmz6d27t9ljtjh+O9lprHb8tuqwptFoCHBzoLjaNi7/VRQj+X//TvR/HjLdkaDpllGBgYFWeTmwEEIIcTbefPPNkz5ma+M3QICbg9WO01Yd1gAC3RzZW623iYX17DQaknsF4nHFFaSnp/PCCy9QUVEBgL+/vym4Nf2Jjo7GwcHqvwIhhBDijNnU+A0EuVnveGzVV4MC5JXX81VRldpltNiYUE/T7SoURaG4uNjs3qDp6emmEzJdXFxISkoyhbfU1FSSkpLw8PBQ8R0IIYQQ586Wx29rY/VhrUZvZHHWEZtJ5tOSfHFzOPXFBeXl5WRkZJgFuJycHPR6PRqNhqioKItZuICAAKudnhVCCCFO1BHHb7VYfVgD+KboKLnlOqy5UA0Q7+PE6FCvs3p9fX09eXl5FrNwlZWVAHTr1q3ZNqq9vfWtByOEEEJA5xi/24NNhLV9xxp4v6BS7TJO68boLvRwdzz9E1tIURR2795Neno6aWlppgBXXFwMgKurK8nJyWYBLikpCXd391arQQghhDhbnXX8bm02EdYURWFpfgVldQarTOcawM/Fnsmx3u3Sqjxy5IhFGzU3N9fURo2Ojm62jSqEEEK0Jxm/W4dNhDWA7ZX1fL7Tek9UHBfuSVQX9U5MrK+vJzc31xTemmbiqqoaP7Pu3bubLSWi1WqJjIyUNqoQQog2JeP3ubOZsAbwVdFR8q2s960B4nycuNIKe91Go5GioiKL8+D27NkDgJubW7NtVDc3N5UrF0II0ZHI+H1ubCqs1eqNvJlbTq3Bekp2tddwe7wPrlZ6BUlzDh8+3Gwb1WAwYGdnZ2qjHj8T161bN7XLFkIIYaNON35XlO5j3Rvz2Pb7OmoqjuDp1534iy9n6G0P4u7t26JjlBXvZMM7L7F98y8cKz+Ml38APZP6cOGNUwmO11o835bGb5sKawAFFfWs2mU906ljwzyJ9rbu6dOWqKurIycnxyzAZWRkmNqogYGBFufBRUZGYmdn/T/kQggh1Hey8fvI3iJemzQSv5BwLrnrEXyDenFgZz6rFz6JvkHH1Hd/wK2Lzyn3vTc3nbenjCUgIpYhk+/DPzSK+ppqcjf8wM6/f+X2t7+2eI0tjd82F9YANpXWsLGkRu0yuCjQjYEBHbdlaDQa2bVrl0Ubde/evQC4u7s320Z1dXVVuXIhhBDWqLnxe9m0azhQmM9/v/gTR5d/x4+qsgO8cGV/el8xnjGPvnDSfSqKwqLxg3Fwcmbq+2stJhFqqypx9exits3Wxm+bDGuKorCxpIY/DtSqVsPA7q4MDnSz6qtH2kpZWZlFgMvPzze1UWNjYy1m4fz9/dUuWwghhMpOHL9rKst5emgMl971KEMm32fx/FVPPUD2um94fH3BScfb/fmZvHLdMK559g20I8aetgZbHL+t90ZYp6DRaBgc6IaTnYZfVJhhs7VE3tr8/PwYPnw4w4cPN22rra21aKN+/fXXVFdXAxAUFGQR4CIiIqSNKoQQnciJ43dZ8U4URcE/LLrZ53cLi6b2aAXHysvw8G3+l/6y4p2Nzw2NPO3xbXX8tsmwBo1f+MAAN7q62LO6uJo6g9KmV5loABd7DZeHeNhMj7s9ubq60rdvX/r27WvaZjQa2blzp9lSIsuXL2f//v0AeHh4kJKSYhbgEhISpI0qhBAd2PHj92s5p57dMo3sp5oFU079nI4wfttsWGsS7e1MTw9H1u6tJq9chwZaNbQ17S/Ox4lLgj1s4qoRa2FnZ0dkZCSRkZFcffXVpu0HDx40uxp1/fr1vPbaaxiNRuzt7Ztto/r5+an4ToQQQrS2aG9n/ntJKos0Gg7u3EbixSMtxu9Du7bj6uWNu3fXk+7Hr1cEAAd3bScoJsm0vSON3zZ5ztrJFFTU82tJDYfqDOcc2ppe7+9iz+AgN6tfMM/W1dbWkp2dbbaob2ZmJseOHQOgR48eFsuJhIWFSRtVCCFsmKIoxMTEcLiikqfXpFGJk2n8PZMLDBb+50IcnV2Y+v5a7O3szMZvf6UWb2/vdnpHbaNDhTVo/NL21+jZeqiWvHIdRsAOMLbgtU3Ps9NAvLczvf1dCHRzsKmTEDsSg8FAYWGhxcUMJSUlAHh6ejbbRnVxcVG5ciGEEKdz5MgRbrjhBlavXo2joyMDBgzg3plPUOkZwO9bs/hu0ZMYdPXc2YKlO/Zlb+WtO8cRGBXPTfdM57J+SbgZ6vj2229Zu3Ytv/zySzu9q7bR4cLa8Wr0RoqrGiip0VNSo6e0pgFdM6nNyQ4C3BwJcnMgwM2BEE9H3Gx4urSjO3DggMWivtu2bTO1UePi4kzhLTU1lZSUFLp2PfkUuhBCiPa1fv16rr32Wg4ePIiiKNx0001oNBp++OEHDh8+TPeAAIZcPppxd8+gxrlLi8bv2v27+ODleWz4eR1lZWUEBgZy/vnnM336dFJTU9v/TbaiDh3WTqQoCjV6Bb2iYDCCvR04aDS4OWhk9szG1dTUkJWVZRbgMjMzqalpvFq4Z8+eFufBhYWFyfcuhBDtSKfT8dhjjzF//nw0Gg1GoxE7OzsefvhhnnnmmZO+rrOP3zZ/gcGZ0Gg0uDt2/C+1M3Jzc+O8887jvPPOM20zGAzs2LHDLMC99dZblJaWAuDl5dVsG9XZWc5PFEKItnDttdeyatUqoDGAAdjb2+Pk5HTK13X28btThTXRudjb2xMTE0NMTAzXXHONaXtpaSkZGRmm5UTWrl3L4sWLURQFBwcH4uPjzQJcSkoKvr4tuzedEEKIk5swYQK//PILR44cMYU1RVFO+Uvyr7/+yuWXX37Sx5vW8+zIOlUbVIiTOXbsWLNt1NraxlW2Q0JCLNqooaGhnWL6XQghWlN1dTWpqakUFhaaWqEvvvgiDzzwQLPPr62tZd++fSfdX2Tk6RfDtXUS1oQ4CYPBwPbt282WE0lLS+PQoUMAdOnSxSLAxcfHn3Y6XwghOrOff/6ZYcOGsXz5cjIzM1m8eDErVqwwW49TmJOwJsQZUBSF0tJSi+VEtm/fjqIoODo6NttG9fE59WXnQgjRGSiKYjq3ePPmzWg0GtN9paVTcXIS1oRoBdXV1c22Uevq6gDo1auX2XIiWq2WkJAQ+cdJCNGprFq1inHjxrFu3TqGDh2qdjk2Q8KaEG1Er9dTUFBgFuDS0tIoKysDwNvb26KNGhcXJ21UIUSHpNfrSUxMpFevXqxZs0btcmyKhDUh2pGiKJSUlDTbRgVwdHQkISHBIsR16dJF5cqFEOLcvPPOO9x6661s2bKFPn36qF2OTZGwJoQVqKqqIjMz0yzAZWVlUV9fD0BYWJhFgOvZs6e0UYUQNqG2tpbo6GjOP/98Pv74Y7XLsTkS1oSwUnq9nm3btlm0UQ8fPgyAj49Ps21UR0dHlSsXQghzL774IjNmzCAvL4+oqCi1y7E5EtaEsCGKorB//35TcGsKcYWFhQA4OTmRmJhoFuCSk5OljSqEUE1lZSXh4eH85z//4fXXX1e7HJskYU2IDuDo0aPNtlF1Oh0A4eHhFrNwwcHB0kYVQrS5xx57jAULFrBjxw6CgoLULscmSVgTooNqaGhoto165MgRAHx9fc2WEtFqtcTExEgbVQjRakpLS4mIiOCee+5h7ty5apdjsySsCdGJKIrC3r17La5G3blzJwDOzs7NtlG9vLxUrlwIYYvuuusuPvzwQ3bu3CmLg58DCWtCCCorKy3aqNnZ2aY2akREhMWivkFBQdJGFUKcVGFhIbGxsTz99NPMmDFD7XJsmoQ1IUSzGhoayMvLs5iFKy8vB8DPz8/iPLiYmBgcHBxUrlwIYQ2uv/56NmzYwPbt23Fzc1O7HJsmYU0I0WKKorBnzx6LALdr1y6gsY2alJRk0Ub19PRUuXIhRHtKT08nNTWVN954g9tvv13tcmyehDUhxDmrqKggMzPTbDmRnJwcGhoa0Gg0REZGWszCBQYGShtViA5q5MiR7Nixg5ycHLloqRVIWBNCtAmdTtdsG7WiogIAf39/iwAXHR0tbVQhbNwvv/zCkCFD+Pjjjxk/frza5XQIEtaEEO1GURSKi4vNlhJJT09n9+7dALi4uJCUlGS2nEhSUhIeHh4qVy6EaAlFURg0aBA6nY6//voLOzs7tUvqECSsCSFUV15eTkZGhtkMXE5ODnq9Ho1GQ1RUlMUsXEBAgLRRhbAyX331FWPGjGHt2rVccsklapfTYUhYE0JYpfr6+mbbqJWVlQB069bNYjmRqKgo7O3tVa5ciM7JYDCQnJxMQEAAP/30k/wy1YokrAkhbIaiKBQVFVkEuOLiYgBcXV1JTk42m4FLSkrC3d1d5cqF6PjeffddJk2axF9//UW/fv3ULqdDkbAmhLB5R44cMbVR09LSyMjIIDc319RGjY6ONgtwqampdO/eXe2yhegw6urqiImJoV+/fnz22Wdql9PhSFgTQnRI9fX15OTkWMzCVVVVARAQEGBxHlxkZKS0UYU4CwsXLuS///0vOTk5xMbGql1OhyNhTQjRaRiNxmbbqHv27AHAzc2t2TaqrL4uxMkdPXqUiIgIrrrqKt5++221y+mQJKwJITq9w4cPm7VR09PTycvLw2AwYGdnR0xMjMUsXLdu3dQuWwir8MQTT/Dcc8+xY8cOgoOD1S6nQ5KwJoQQzairq7Noo2ZkZJjaqIGBgc22UWVdKdGZHDx4kIiICKZMmcILL7ygdjkdloQ1IYRoIaPRyK5du8wCXFpaGvv27QPA3d2d5ORks0V9ExMTcXV1VblyIdrGvffey/Lly9m5cyddu3ZVu5wOS8KaEEKco0OHDlks6pufn29qo8bGxlrMwvn7+6tdthDnpKioiOjoaJ544gkeffRRtcvp0CSsCSFEG6itrW22jVpdXQ1AUFCQxaK+4eHh0kYVNmPixImsXbuWwsJCWcuwjUlYE0KIdmI0GiksLLS4GnX//v0AeHh4kJKSYjYDl5iYiIuLi8qVC2EuKyuLlJQUFi9ezNSpU9Uup8OTsCaEECo7ePBgs21Uo9GIvb19s21UPz8/tcsWndiVV15Jbm4ueXl5ODo6ql1OhydhTQghrFBNTQ05OTmmpUSa2qg1NTUABAcHWwS4sLAwaaOKNvfbb79x4YUX8uGHH3LttdeqXU6nIGFNCCFshMFgaLaNWlJSAoCnp6dFGzUhIUHaqKLVKIrChRdeSHV1NVu3bpVfDtqJhDUhhLBxBw4cICMjw2wWbtu2bSiKgr29PXFxcWbLiaSkpMgyC+KsfPfdd1xxxRV8//33XH755WqX02lIWBNCiA7o2LFjZGdnm83AZWZmmtqoPXv2bLaNqtFoVK5cWCuj0YhWq8XX15f169fLz0o7krAmhBCdhMFgYMeOHWa31UpLS+PgwYMAeHl5WQS4+Ph4nJ2dVa5cWIMPPviAG2+8kU2bNjFw4EC1y+lUJKwJIUQnV1paanEeXEFBAYqi4ODgQHx8vFmAS0lJwdfXV+2yRTvS6XTExMSQkpLCl19+qXY5nY6ENSGEEBaOHTtGVlaW2W21MjMzqaurAyAkJMRsQV+tVkuvXr2kNdZBLV68mHvvvZfMzEwSEhLULqfTkbAmhBCiRfR6Pdu3b7e4N+qhQ4cA6NKlS7NtVCcnJ5UrF+eiurqaiIgIRo4cybJly9Qup1OSsCaEEOKsKYrSbBt1+/btKIqCo6OjWRs1NTWVlJQUvL291S5dtNBTTz3F008/TUFBAb169VK7nE5JwpoQQohWV11dTWZmplmAy8rKMrVRQ0NDLWbhQkJCpI1qZcrKyggPD+fWW29lwYIFapfTaUlYE0II0S70ej0FBQUWbdSysjIAvL29LQJcXFyctFFV9N///pe33nqLwsJC/P391S6n02rTsLZ8+XLuu+8+Kioq2uoQABQVFREWFkZaWhparbZNjyWEEKL1KIpCSUmJ2XIi6enp7NixAwAnJycSEhIsrkbt0qWLypV3fMXFxURFRTFz5kxmzZqldjmdWquFtdDQUO677z7uu+8+07ba2lqqqqro1q1baxwCgEmTJlFRUWF26bDBYODQoUP4+fnh4ODQasdqa0OGDEGr1bJw4cJW33dzn5MQQtiKqqqqZtuo9fX1AISFhVnMwvXs2VPaqK1o8uTJfPvttxQWFuLp6al2OZ1amyYbV1dXXF1d2/IQANjb2xMQENDmxxFCCNE+PD09GTRoEIMGDTJt0+v1bNu2zayF+vLLL3P48GEAfH19LQJcbGwsjo6Oar0Nm5Wbm8u7777LwoULJahZA+UMfPXVV0qfPn0UZ2dnpWvXrsr//d//KYqiKBdddJECmP1RFEVZtmyZ0qVLF0VRFCU/P18BlLy8PLN9vvjii0qvXr0Uo9Go6PV6ZfLkyUpoaKji4uKiREdHKwsXLjQ9d/bs2RbHWb9+vbJr1y4FUNLS0kzP3bBhg9KvXz/FyclJCQgIUGbMmKHodDqlWmdQyuv0yqALByu3T52m3PfAg4qPj4/SvXt3Zfbs2S3+LHbv3q1ceeWViru7u+Lp6an85z//UUpLS02P33TTTcpVV11l9pp7771Xueiii0yPn/hedu3apaxfv14BlG+//VZJTk5WnJ2dlf79+yuZmZlmn0NKSorZvl966SWlV69ep/ychBCiozEajcqePXuUb775RnnqqaeUcePGKREREaZ/+5ycnJTevXsrkydPVl5++WVl48aNSmVlpdplW70xY8YooaGhSl1dndqlKIrS+D03jd+Ha/VKeZ1eqdYZFKPRqHZp7aLFM2vfffcdY8eOZebMmbz//vvodDq+++47AFatWkVKSgq33347t912W7Ovj4mJoU+fPqxYsYKnnnrKtP3DDz/kuuuuQ6PRYDQaCQ4O5pNPPsHPz49NmzZx++23ExgYyPjx43nwwQfJy8vj6NGjprVefH192b9/v9mx9u3bx8iRI7lh4k3MfuVttmbn8sL0aaRVGLn4jocA2F+jZ8t773LB9Xdyx/LVVOSnMefhqfRM7se1V16Om4PdqQIuY8aMwd3dnV9++QW9Xs/UqVO55ppr2LBhQ4s+z0WLFlFQUEBiYiJz5swBwN/fn6KiIgCmT5/OokWLCAgI4NFHH+XKK6+koKCgRb8hnuxzEkKIjkaj0RAcHExwcDBXXHGFafvRo0ct2qgffPABOp0OgPDwcLPlRLRaLT169JA2KvDnn3/y5Zdf8t5776l2q7EavZHdVQ2U1ugpqWn8X53R8nlOdhDg5kCgmyMBbg708nQ85fhtq1oc1p555hkmTJjAk08+adqWkpICNAYBe3t7PD09T9mOvP7661m8eLEprBUUFPDPP//w3nvvAeDo6Gi2/7CwMDZt2sQnn3zC+PHj8fDwwNXVlfr6+pMeR1EUnl/4Cj4BPQi/7UmyNBqcewdx8R0P8cPLc7jotgexs2v8IgMj4xl+x3QAuvSMoMd7b/L+N2s4HHYecT5O9PZ3JcjNweI/3p9++onMzEx27dpFz549AXj//fdJSEjg77//pl+/fqf9PLt06YKTkxNubm7NvpfZs2dzySWXAPDuu+8SHBzMF198wfjx40+775Z8TkII0ZF5eXlxwQUXcMEFF5i2NTQ0kJ+fbxbgXnrpJcrLywHo2rWrRRs1JiamU7VRFUXh4YcfJikpieuuu67dj72/Rs/WQ7XkleswAnZAMxnNRGeE4mo9e6v1puefavy2VS0Oa+np6SedNWupCRMmMH36dP78808GDBjAihUrTCtcN3n99dd5++232b17N7W1teh0uhZf4bm7SseW/Ap+/iebgIQ+KP/7koxAqLY/uppjHD2wH+/AYAACouLNXu/p153qI2UYgdxyHTnlOvxd7Bkc5EZUl39/u8jLy6Nnz56moAYQHx+Pt7c3eXl5LQprp3P8TXJ9fX2JiYkhLy/vnPcrhBCdlaOjI0lJSSQlJXHjjTcCjQFh7969ZgFu1apVvPjiiwA4OzuTmJhoNguXnJzcYc/jWrNmDb/88gvffPMN9vb27Xbcgop6NpbUUFZnQENjDxtOHdSOZzzuf081ftuqFoe11rhQIDAwkIsvvpgPP/yQAQMGsHLlSu644w7T45988gn3338/L774IgMHDsTT05MXXniBzZs3n3K/dfrGr2ndvmP08DCgoMAJaVppuuj1uO12Dua/LWk0GhSlcV9NPyhldQY+31lFnE89lwZ74Opgh6Iozab147fb2dn9e8z/aWhoOOX7OJ223LcQQnRGGo3G9Mv36NGjTdsrKirM2qhbt27lvffeM/1bGxkZaTELFxQUZNMzOUajkUceeYRBgwYxatSodjlmrd7I2r3V5JXraPrkznWJilON37aqxWEtOTmZdevWcfPNNzf7uJOTEwaD4bT7uf7665kxYwbXXnsthYWFTJgwwfTYr7/+yvnnn8/UqVNN2woLC095nIKKej7cXmn6uwJ0C4sm5+dvzcLT7oy/cXb3wKtbYIve7/H7A8gv11F0tJzLQzyIj4+nuLiYPXv2mGbXcnNzqaysJC4uDmg8/yw7O9tsX+np6WbT6af6zP78809CQkIAKC8vp6CggNjYWNO+S0tLzd5fenq62etb+n0IIYSw5O3tzeDBgxk8eLBpm06ns2ijzp8/37SWqJ+fn1l4S01NJTo62maWlPrkk09IT0/n119/bZfQWVBRz+riauoMjSNtay/62tz4He1tm7NsLY6Zs2fPZuXKlcyePZu8vDyysrKYN2+e6fHQ0FA2btzIvn37TKtRN2fs2LEcPXqUO++8k4svvpgePXqYHouMjGTLli2sWbOGgoICHn/8cf7++2+z14eGhpKZmUl+fj7f5xTzacER6o3mX/GA8ZOpKN3P188/zMFd28ndsJp1r8/jguvvNJ2vdqYUoNagsGpXFe6J55OcnMz111/P1q1b+euvv5g4cSIXXXQRffv2BWDo0KFs2bKF9957j+3btzN79myL8BYaGsrmzZspKiqirKwMo/HfCd85c+awbt06srOzmTRpEn5+fowZMwZoXJ/t0KFDzJs3j8LCQl599VVWr17d7Oe0bds2ysrKZOZNCCHOkZOTE8nJyUycOJEFCxbw888/c+TIEYqKivjyyy+ZNm0aHh4efPbZZ9xwww0kJCTg6elJ//79uf3221myZAmbNm2iurpa7bdioaGhgccee4wrrrjC7Dy/tqAoCptKa1i1q4pag9LqIc3iePw7fv9RWmPRmbIFLU4uXl5efPrpp3z99ddotVqGDh1q1p6cM2cORUVFREREnPKWFF5eXowePZqMjAyuv/56s8emTJnC2LFjueaaazjvvPM4fPiw2SwbwG233UZMTAy9+/ZjVGIvdmf8ZXGMLt0CmfTKh+zNSePlCUP48tkH6TvmOi6+9YGWvt1T2lhay8NvfIiPjw+DBw9m+PDhhIeH8/HHH5uec9lll/H444/z0EMP0a9fP6qqqpg4caLZfh588EHs7e2Jj4/H39+f4uJi02PPPfcc9957L3369KGkpISvv/7adMuVuLg4lixZwquvvkpKSgp//fUXDz74YLOfU9++ffH39+f3339vlfcuhBDiXxqNhl69enHVVVcxe/ZsvvjiC3bt2kV5eTkbNmzgueeeM118dt999zFo0CC8vLyIjo5m/PjxPPvss3z//ffs379f1RDx9ttvs3PnTp555pk2PY6iKPxSUsPGkpo2Pc7JNB3b1gKbTd4bdFOpel/08S4KdGNggFur7nPDhg1cfPHFlJeX4+3t3ar7FkIIoR6dTkdeXp7ZbbXS09OprGw8ladbt24W58FFR0e3+Yn+x44dIzIykuHDh/P++++36bE68vjdlmyjkX6cpitGrMEvJTV0dbG32R64EEKI9uPk5ERKSopp2StonGnavXu3WXj7+OOPTacZubq6kpSUZBbgkpKS8PDwaLW6mu4C0bTmZ1uR8fvs2dTMWq3eyJu55dQa2rbktO8/48tn/tvsY96BPbn/s99Mf3e113B7vE+rXWUiM2tCCCHKy8vJyMgwu8F9bm4uer0ejUZDVFSUaTHfpj9ns67mkSNHCA8PZ+LEibz88stt8E4atdf4fSZae/xuSzYV1r4qOkp+ua7NT0asP1ZN1eGDzT5m7+CIT9C/66tpaFyA78pQrzauSgghRGdWX19Pbm6u2Sxceno6R48eBaB79+4WbdSoqKhTtlEfeughlixZQmFhId27d2+z2ttr/D4TtjR+20xYK6ioZ9WuKrXLOKlx4Z4dYuE9IYQQtkNRFIqKiiwCXNMFa66uriQnJ5vdVispKQk3Nzf27t1LVFQUDz30kNndg1qbjN/nzibCmqIovJNfweE6g1Wl8iYawM/Fnsmx3ja9IKIQQoiO4fDhw6Y2atOf3NxcDAYDdnZ2REdHo9PpKC0t5f3332fQoEFnNbNWUVGBi4sLLi4uzT4u43frsImwtu9YA+8XVJ7+iSq7MboLPdw7zz3khBBC2I66ujpTG3X9+vV88MEHODs7U19fD0BAQIDFor4RERGnbKOmpqZSUVHBl19+aXbhRBMZv1uHTYS1b4qOkmtlve4TaYB4HydG20DvWwghROf2n//8h82bN5Ofn09paanFciJ79+4FwN3d3dRGbfqTmJiIm5sbtbW1eHh4oCgKjo6OLFmyhMmTJ5vNUMn43TqsPqzV6I0szjrS4pu5qskOmJbki5sNXFkihBCic/r777/p378/y5YtY9KkSc0+p6yszKKNmpeXZ2qjxsTE0LNnT9auXWv2uhtuuIHXX38dd3d3Gb9bkdWHtbzyer4qst4TE080JtSTWB/rPlFRCCFE5zV8+HBKSkrIzMw8owV36+rqyM7ONoW3H374weL+3QC+vr6sWbMG94gkGb9biXVGyOOU1uitv8j/saOxXiGEEMIa/fTTT6xbt45nnnnmjO+M4OLiQt++fbn11ltZvHgxl156qdlN6pvuvX3kyBE+//xzGb9bkdXfwaCkpsEmplABjMB+K/6yhRBCdF6KovDwww8zYMAArrrqqnPeX1paGnp945jn4+PDqFGjGDlyJJdeeildu3blw+0VMn63EqsOa4qiWHXSbU5pjR5FUaz6EmAhhBCdz2effcY///zDhg0bWmWMmjJlCiNHjuTyyy+nd+/eppk1kPG7tVl1WKvRK+hsJZb/j86oUKNXcHe0vi9bCCFE59TQ0MDMmTMZMWIEF110Uavs86abbjrpYzJ+ty6rDmsNRqu+9uGk9NZ9zYYQQohOZvny5Wzfvp1PPvmkXY4n43frsupz/2z0u8ZgY79NCCGE6Lhqamp44oknuPbaa9Fqte1yTBm/W5dVhzU765uJbBF7q/5UhRBCdCaLFy/m4MGDzJkzp92OKeN367LSsho52ui37WCFJycKIYTofMrLy5k7dy633XYbkZGR7XZcGb9bl1Wfs+bmoMHJDps6SbH+WDXjRt9gdm+1yMjIM17PRgghhDhXL7zwAjqdjscff7xdj2uL47eTnQY3BwlrZ0yj0RDg5kBxtY1c/qsoONSU4+TkxIcffsjzzz8PgJubm8W91ZKSknBzc1O5YCGEEB3V/v37WbhwIffffz+BgYHtemybG7+BADcHq1y2A2zgdlPr9x3j74O1NrGwnh3Qv5srQ3q4A3D48GGLe6vl5uaa7q0WHR1tFuC0Wi3du3dX900IIYToEO68804+/vhjdu7cibe3d7sf35bHb2tj1TNr0Jh0beGLhsYVkAPc/v1Iu3btytChQxk6dKhpW11dHTk5OWYB7rvvvqOqqvH+aYGBgRYBLjIy0myxQSGEEOJUtm/fzltvvcVzzz2nSlAD2x6/rY3Vz6zV6I0szjpiE1+4HTAtyRc3hzMLVkajkV27dpkFuPT0dPbu3QuAu7u7RRs1MTFR2qhCCCGaNWHCBH777Te2b9+Oq6urKjV0hvG7vVh9WAP4pugoueU6rLlQDRDv48ToUK9W22dZWZlFgMvPzze1UWNiYkhNTTULcf7+/q12fCGEELZn69at9OnTh7feeotbb71V1Vo66/jd2mwirO071sD7BZVql3FaN0Z3oYe7Y5seo7a21qKNmpGRQXV1NQBBQUEWbdSIiAhpowohRCcxYsQIioqKyM7OxsFB3daejN+tw3obtMcJcnPA38WesjqDVaZzDeDnYk9QO/S7XV1d6du3L3379jVtMxqN7Ny5k/T0dNLS0khPT2f58uXs378fAA8PD1MbtWkmLiEhQbWpcSGEEG1j/fr1rFmzhs8++0z1oAYyfrcWm5hZA9heWc/nO6vULuOkxoV7EtXFWe0yzBw8eNDiatT8/HyMRiP29vbExsZazML5+fmpXbYQQoizoCgKAwYMwGg08tdff1nNMhQyfp87mwlrAF8VHSXfynrfGiDOx4krrbjXfbza2lqys7NN4S0tLY3MzEyOHTsGQI8ePczCW2pqKmFhYdJGFUIIK/fFF18wduxYfvrpJ4YNG6Z2OWY6y/it0Wj44osvGDNmTKvtE2wsrNXqjbyZW06twXpKdrXXcHu8D65WegVJSxgMBgoLCy0uZigpKQHA09OTlJQUsxCXkJCAi4uLypULIUTHMmnSJCoqKvjyyy/P6HV6vZ7k5GSCg4NZu3Ztq9c1ZMgQfvnlF1auXMmECRNM2xcuXMjChQspKio65evbevxe/85L/Pjac1x616MMufles8fevO0qAqMTGT39GdO2nVt+563bx7D/0GEC/XxbrY7S0lJ8fHxwdm7dmTrrbtKewNXBjstDPFi1y3qmUy8P8bDpoAZgb29PdHQ00dHRjB8/3rT9wIEDpjZqWloaP/30E6+++iqKomBvb09cXJxFG7Vr164qvhMhhOic3nvvPfLy8njvvfdadb86nQ4nJycAXFxceOyxxxg3bhyOjmd2Mv6pxm9DQwP2Z7i/E/3z9UoG3zSNLV99aBHWTldXawoICGjV/TWxuZQR7e3M4EDrWF/sokA3or2tu899Lrp3786ll17KQw89xMqVK8nLy6Oqqoo///yTV199lQsuuIAdO3Ywa9Yshg8fjp+fHyEhIVx55ZXMmjWLVatWsXPnTmxo8lYIIazWggULSEpKwt3dnZ49ezJ16lSqq6upq6vj8ccfx8HBwWKG65tvvsHd3d208Pq+ffu45ppr8PHxoWvXrlx11VVmr5k0aRJjxoxh7ty5BAUFER0dbXrs2muvpbKykrfeeuuUdb722mtERETg5ORETEwM77//PvDv+P1Ib382f7ac9+6/kVnn9+Lntxfw0+vzeHnCELZ8uYLnRmqZPagXXz47HaPBwC/LX+GZS+J5elgc699eYHG8nf/8TkN9HZdMeZiG2hp2/bPJ9Nins6ex659NbFr5Jo/09ueR3v6U7y/mrdvHAODj44NGo2HSpElA4wziPffcw0MPPYSvry8BAQE88cQTLfyGGtugTbOiRUVFaDQaVq1axcUXX4ybmxspKSn88ccfpucvX768Rfu1qZm1JgO7u9JgVPjjQK2qNQzo3vmupnR3d+e8887jvPPOM20zGAzs2LHDrIX65ptvcuDAAQC8vLyabaO29jSxEEJ0ZHZ2drz88suEhoaya9cupk6dykMPPURkZCQHDhxg3LhxLFu2jKuvvtr0mqa/e3p6UlNTw8UXX8yFF17Ixo0bcXBw4Omnn2bEiBFkZmaaZtDWrVuHl5cXP/74o9kv215eXjz66KPMmTOHm266CXd3y1szffHFF9x7770sXLiQ4cOH8+2333LzzTcTHBzMxRdfzMD/jZs/vT6Py6bN5Ir/PoXGzp5/vl7J4b1FbNu0jpsXf8yRPUWseGgyR/btxq9XBLe/9TW7M/7i8yfvJaL/YEKS/10RYcuXH5Jy2VjsHR1JGTGWv79aQVif8wEY/eCzlO3eSfeIWC65cwYAw2J70v+zz7j66qvZtm0bXl5eZqsjvPvuuzzwwANs3ryZP/74g0mTJjFo0CAuueSSs/reZs6cyfz584mKimLmzJlce+217Nix44yu1rXJsKbRaBgc6IaTnYZfSmra/fgXBboxMMA6Zvesgb29PTExMcTExHDNNdeYtpeWlpoFuLVr17J48WIURcHBwcHURm1aTiQlJQVf39Y7d0AIITqS++67z/T/w8LCeOqpp5gyZQoAt9xyC7fccgvnn38++/fvJygoiLKyMr799lt+/PFHAD766CPs7Ox4++23TVeKLlu2DG9vbzZs2MCll14KNP5S/vbbb5vC2/GmTp3KokWLWLBgAY8//rjF4/Pnz2fSpElMnToVgAceeIA///yT+fPnc/HFF5uOO2rceLRjrjd7rWJUuHr2yzi7e9A9PIbwvoMo213IpFca6/YPjWTju6+w85/fTWGtrrqK7HXfcufy7wDQjrya12++gisfeg4XD09cPL2wd3TE0cUVT7/upvF7Q2HjKTvdunWzuB1XcnIys2fPBiAqKorFixezbt26sw5rDz74IKNGjQLgySefJCEhgR07dhAbG9vifdhkWIPGwDYwwI2uLvasLq6mzqC06VUmGsDFXsPlIR4duvXZmgICAhgxYgQjRowwbTt27BhZWVlmIe6zzz6jtrZxljQkJMTiPLjQ0FCruQRdCCHUsn79ep599llyc3M5evQoer2euro6nJ2dmTVrFj169CAhIYH33nuPhx9+mPfff5+QkBAGDx4MwD///MOOHTvw9PQ0229dXR2FhYWmvyclJTUb1ACcnZ2ZM2cO06ZN484777R4PC8vj9tvv91s26BBg1i0aJHZttEXDaRfmKdp/AbwCeqJs7uH6TkeXf2xs7c3W43Aw9ef6iNlpr9n/PA5vsG9CIxOBCAoJgnf4F5krvmC/uMmmp7nYKdhbJhni8bv5ORks78HBgZy8ODB076uJfsLDAwEGpfW6hRhrUm0tzM9PRxZu7eavHIdGmjV0Na0vzgfJy4Jtv2LCdTm7u7OgAEDGDBggGmbwWBg+/btpgV909PTee211zh06BAAXbp0MWujpqamEh8ff9J/TIQQoqPZvXs3I0eOZMqUKTz11FP4+vry3Xff8cADDzBlyhR69OgBwK233srixYt5+OGHWbZsGTfffLPpl12j0UifPn1YsWKFxf6Pv1Vhc+3N491www3Mnz+fp59+mtDQUIvHT/zlWlEUi23u7u64Vh1k6eRxjPzv0wDYn9AW1Gg02DuccOGBRoNi/Pduo1u++pCDhfnM7Pfvif2K0cjfX63gvHETUQB3BzuSfJ1bPNFy4sUTGo0Go/Hs73B6/P6O/y7OhM2HNWi8muOqUC/ivOv5taSGQ3WGcw5tTa/3c7FncJCb1S+YZ8uaFuiNjY3l2muvBRr/4z6xjfrDDz/wyiuvoCgKjo6OxMfHm83ApaSk4OPjo/K7EUKI1rdlyxb0ej0vvviiaaZp4sTGmaPj26M33HADDz30EC+//DI5OTncdNNNpsd69+7Nxx9/TLdu3fDyOvu1xezs7Jg7dy5jx461mF2Li4vjt99+M9UGsGnTJuLi4sye9/333zNhwgTq6+vxeeNZkgZcQMH/gkxLx+/S7bnsy03ntje/xLXLv//211dV8satV1K/ZxvXXtSXn71c0Sjm4ajpl32DwXAG71w9HSKsNYn2diaqixP7a/RsPVRLXrkOI42XvLYkwzY9z04D8d7O9PZ3IdDNQVpwKtBoNAQGBhIYGMjll19u2l5dXW1qozbNxH388cfU1dUB0KtXL4s2aq9eveQ7FELYjMrKStLT0822+fv7o9freeWVVxg9ejRffPEFmzdvBjA758rHx4exY8cyffp0Lr30UoKDg02PXX/99bzwwgtcddVVzJkzh+DgYIqLi1m1ahXTp083e+7pjBo1ivPOO4833niD7t27m7ZPnz6d8ePH07t3b4YNG8Y333zDqlWr+OmnnwDIz88HMLuitF+/fri7OODrYs+N0V1M4/fp/P3lCoITehPW53yL8XvLeQM48vOnRF05iNDQUDZv3kxRUREeHh74+vqaxoVvv/2WkSNH4urqioeHx+kOqZoOFdagcZDv4e5ID3dHhgUbKa5qoKRGT0mNntKaBnTNpDYnOwhwcyTIzYEANwdCPB1xk3anVfLw8GDgwIEMHDjQtE2v11NQUGA2C/fqq69SVtZ4XoO3t7dFgIuLi5M2qhDCKm3YsIHU1FSzbTfddBMLFizg+eef55FHHsHb25suXbpQWWl5k/RbbrmFDz/8kMmTJ5ttd3NzY+PGjcyYMYOxY8dSVVVFjx49GDZs2FnNtD3//POcf/75ZtvGjBnDokWLeOGFF7jnnnsICwtj2bJlDBkyhFdffdVsFhAaOytdu3Y1/cJ9/Pj9h4cjh3T2hHg4Wozf+gYd6as/Y+ikuwnxsBy/C/5zNXPnzmXevHk8+OCD3HTTTcTHx1NbW8uuXbsIDQ3lySef5OGHH+bmm29m4sSJLV5GQw02dQeDc6UoCjV6Bb2iYDCCvR04aDS4OWhk5qWDURSFkpISsxm49PR0duzYATSeQ5CQkGDRRj3xqiAhhLA2GRkZpKam8tprr3HHHXdYPL5ixQruvfde9u/fb1W/lA4dOpT169dbbH/jjTcsLko4UUcdv5cvX25a4+1UOtzM2qloNBrcHW33SxUtp9FoCAoKIigoiJEjR5q2V1VVkZmZaTYLt3LlSurr6wEIDQ01LSXS9Kdnz542/Y+BEKJjefTRR4mIiLCYOaupqWHXrl3MnTuXO+64w6qCGsCaNWt45ZVXmDFjBnq93rS9JUs2dfbxu1OFNSE8PT0ZNGgQgwYNMm3T6/Vs27bN7Ob2L7/8MocPHwYazwFpro16prdbEUKIc7Vx40a+//57PvroI4t/g+bNm8czzzzD4MGDeeSRR1Sq8OQcHR0ZNGgQer2e/v3789dffwEtC2vWYMWKFc3OZELj+dI5OTltduxO1QYVoqUURWHfvn0WN7dvWovIycnJ1EZtmolLTk6mS5cuKlcuhOioFEXhggsuoK6ujr///tts/TFbYDQaGTBgAA0NDWzZsoXNmzezbNkyXnzxxXO6OrW9VFVVme7McyJHR0d69erVZseWsCbEGTh69KhFGzUrKwudrvHKpfDwcItZuODgYGmjCiHO2ddff81VV13FmjVrTHcbsCXvvPMOt956K7/99ptZd0OcnoQ1Ic5RQ0MD+fn5FrNwR44cARqn+I8Pb6mpqcTExEgbVQjRYgaDgZSUFLp168a6dets7hfA8vJyoqOjGTFihOnG7qLlJKwJ0QYURWHv3r0WAW7nzp1A4y1bEhMTzUJccnKyTbQChBDt77333uOmm25i8+bN9O/fX+1yztg999zDsmXLKCgoMN1ySbSchDUh2lFlZSUZGRlmAS47O5uGhgYAIiIiLNqoPXr0sLnfooUQrae+vp6YmBj69OnD559/rnY5ZywzM5PU1FSef/55HnzwQbXLsUkS1oRQmU6ns2ijpqWlUVFRAYCfn59FgIuJicHBQS7mFqIzWLRoEQ888ADZ2dkWt22ydoqiMGTIEA4ePEhGRobVLSdiKySsCWGFFEWhuLjYoo1aVFQENLZRk5KSLNqonp6e6hYuhGhVVVVVhIeHc+WVV/LOO++oXc4ZW7lyJddddx1r167lkksuUbscmyVhTQgbUlFRYdFGzcnJMbVRIyMjzZYT0Wq1BAYGShtVCBv15JNPMnfuXLZv307Pnj3VLueMVFdXExMTw3nnnceqVavULsemSVgTwsbpdDry8vLMbquVnp5uumegv7+/RRs1Ojpa2qhCWLmDBw8SERHBHXfcwfz589Uu54w9/PDDLFq0iLy8PEJDQ9Uux6ZJWBOiA1IUhd27d1u0UXfv3g2Ai4uLWRs1NTWVpKQkPDw8VK5cCNHkvvvuY9myZezcuZOuXbuqXc4ZKSgoIDExkZkzZzJ79my1y7F5EtaE6ETKy8tNbdSmmbjc3Fz0ej0ajYaoqCiLWbiAgABpowrRzoqKioiJiWHWrFnMnDlT7XLOiKIojBw5kvz8fHJzc3F1dVW7JJsnYU2ITq6+vp7c3FyLWbijR48C0K1bt2bbqPb29ipXLkTHddNNN7FmzRp27NhhczPeTXda+OKLLxgzZoza5XQIEtaEEBYURaGoqMgiwBUXFwPg6upKcnKyWYBLSkrC3d1d5cqFsH3Z2dkkJyfzyiuvcNddd6ldzhmpq6sjPj6e6OhoVq9eLbPyrUTCmhCixQ4fPmxxNWpubi4GgwGNRkN0dHSzbVQhRMtdddVVZGdnk5eXZ3Prkj311FM89dRTZGVlERMTo3Y5HYaENSHEOamrq2u2jVpVVQVA9+7dzZYS0Wq1REZGShtViGZs2rSJQYMGsWLFCq677jq1yzkju3fvJi4ujrvvvpvnn39e7XI6FAlrQohWZzQa2bVrl0WA27t3LwBubm7NtlHd3NxUrlwI9SiKwkUXXURlZSVpaWnY2dmpXdIZufrqq9m0aRPbtm2TBbpbmYQ1IUS7KSsrs2ij5uXlYTAYsLOzM7VRj5+J69atm9plC9Euvv/+e0aNGsV3333HyJEj1S7njKxbt47hw4fzwQcfcP3116tdTocjYU0Ioaq6ujqys7PNAlxGRgbV1dUABAYGWpwHFxkZaXOzDkKcitFoJDU1FW9vbzZs2GBTJ+Y3NDSQkpJC165d2bhxo03VbiskrAkhrI7RaGTnzp0WbdR9+/YB4O7u3mwbVdZzErZqxYoV3HDDDfz++++cf/75apdzRhYsWMD06dPZunUrKSkpapfTIUlYE0LYjEOHDpGRkWF2a638/HyMRiN2dnbExsZazML5+/urXbYQp6TT6YiNjSUpKYmvvvpK7XLOSGlpKdHR0UycOJHFixerXU6HJWFNCGHTamtrzdqoaWlpZGZmcuzYMQCCgoIsAlxERIS0UYXVePXVV7n77rvJysoiISFB7XLOyE033cR3331HQUEBvr6+apfTYUlYE0J0OEajkcLCQrMAl56eTklJCQAeHh6kpKSYBbiEhARpo4p2V11dTWRkJCNGjGD58uVql3NGmpYZefPNN7ntttvULqdDk7AmhOg0Dhw4YHE16rZt2zAajdjb2zfbRvXz81O7bNGBPf300zz11FNs27aN0NBQtctpMYPBQL9+/bCzs2Pz5s2ybmIbk7AmhOjUampqmr0ataamBoAePXpYLCcSFhYmbVRxzsrKyoiIiODmm29m4cKFapdzRt544w2mTJnCH3/8wYABA9Qup8OTsCaEECcwGAzs2LHD4mrU0tJSADw9PZtto7q4uKhcubAlDz74IG+88QY7d+60qQthDh8+THR0NFdeeSXLli1Tu5xOQcKaEEK0UGlpabNtVEVRsLe3Jy4uzhTeUlNTTWtPCXGi4uJioqOjeeSRR5g9e7ba5ZyRqVOnsmLFCgoKCujevbva5XQKEtaEEOIcHDt2jKysLLMAl5mZSW1tLQA9e/a0OA8uLCxMFg7t5G655Ra++eYbCgsLberWTGlpafTt25cXX3yR++67T+1yOg0Ja0II0coMBgPbt283C3BpaWkcPHgQAC8vr2bbqM7OzipXLtpDXl4eiYmJvPTSS9xzzz1ql9NiiqJw4YUXUl5eTnp6Oo6OjmqX1GlIWBNCiHZSWlpqsZzI9u3bURQFBwcH4uPjzQJcSkqKrF3VAY0dO5atW7eybds2mwroH3zwATfeeCPr1q1j6NChapfTqUhYE0IIFVVXVzfbRq2rqwMgJCTELMClpqbSq1cvaaPaqM2bNzNgwADeffddJk6cqHY5LXb06FFiYmK48MIL+eSTT9Qup9ORsCaEEFZGr9c320Y9dOgQAF26dLE4Dy4+Ph4nJyeVKxenoigKQ4cOpaysjPT0dJtam2z69OksWbKEvLw8QkJC1C6n05GwJoQQNkBRFEpKSiyWE9m+fTsAjo6OzbZRfXx8VK5cNFm7di2XXXYZX3/9NaNHj1a7nBbLy8sjOTmZJ554gpkzZ6pdTqckYU0IIWxYVVWVRRs1KyvL1EYNDQ21mIULCQmRNmo7MxqN9O3bF1dXV3777Teb+fwVReHSSy9l586d5OTkyFqCKpGwJoQQHYxer2fbtm0WbdTDhw8D4O3tbRHg4uLipI3ahj7++GMmTJjAxo0bufDCC9Uup8VWrVrFuHHj+Oabb7jiiivULqfTkrAmhBCdgKIo7N+/36KNumPHDqCxjZqQkGB2W62UlBS6dOmicuW2r6Ghgfj4eGJiYvj222/VLqfFampqiI+PJyEhge+++07tcjo1B7ULEEII0fY0Gg09evSgR48ejBo1yrT96NGjZGVlmZYSSU9P58MPP6S+vh6AsLAwi1m4nj172kwbzxq88847FBYW8vnnn6tdyhmZN28eJSUl/Pjjj2qX0unJzJoQQggzDQ0NzbZRjxw5AoCPj4/FciKxsbGySGozampqiIiIYNiwYXzwwQdql9Niu3btIi4ujgceeIBnn31W7XI6PQlrQgghTktRFPbt22fRRi0sLATAycmJxMREsxCXnJzc6duozz33HLNmzSI/P5/w8HC1y2mx//u//2PLli3k5+fj7u6udjmdnoQ1IYQQZ62yspLMzEyzAJednY1OpwMgPDzcoo0aHBzcKdqoR44cITw8nBtvvJFXXnlF7XJabM2aNYwYMYKPPvqIa665Ru1yBBLWhBBCtLKGhgby8/Mt2qjl5eUAdO3a1SLAxcTEdLg26owZM3j11VcpLCyke/fuapfTIjqdjqSkJIKCgvj55587Rai2BRLWhBBCtDlFUdi7d6/ZfVHT09PZtWsXAM7Ozs22Ub28vFSu/Ozs27ePyMhIpk+fzpw5c9Qup8XmzZvHo48+Snp6OomJiWqXI/5HwpoQQgjVVFRUNNtGbWhoACAiIsJsORGtVktQUJDVz/jccccdfP755xQWFtrMeXv79+8nJiaGyZMns2jRIrXLEcexubA2ZMgQtFotCxcuNNv+5Zdf8n//9380vR2dTseiRYtYuXIl27Ztw8HBgdDQUEaPHs3UqVMJCgo67bEmTZrEu+++C4CDgwO+vr4kJydz7bXXMmnSJOzs7Fr9/QkhRGen0+nM2qhNM3EVFRUA+Pn5NdtGdXCwjtWotm3bRkJCAvPmzeOBBx5Qu5wWu+GGG1i7di0FBQV4e3urXY44jnX8ZLey+vp6Lr30UjIzM3nyyScZNGgQXbp0obCwkC+//JJXXnmFuXPntmhfI0aMYNmyZRgMBg4cOMAPP/zAvffey2effcbXX39tNf84CCFER+Hk5ERycjLJyclMnDgRaGyjFhcXm83AffbZZ8yfPx8AFxcXkpKSLNqoHh4e7V7/448/TlBQEFOnTm33Y5+tX3/9lRUrVvDOO+9IULNCHTJpvPTSS/z2229s2bKF1NRU0/aIiAguGHoJOoORI3UG7DTgaKfBzUFz0il1Z2dnAgICAOjRowe9e/dmwIABDBs2jOXLl3Prrbe2y3sSQojOTKPR0KtXL3r16sVVV11l2l5eXm7WRv37779Zvnw5DQ0NaDQaIiMjLWbhAgMD26yNumXLFj799FOWLl1qM/fR1Ov1TJs2jf79+zNp0iS1y2mWoijU6BUajApGhRaN3x1JhwxrK1eu5JJLLiEmKYW88npKa/SU1DRQWqNHZ7R8vpMdBLg5EOjmSICbA708HXFzOHmLc+jQoaSkpLBq1SoJa0IIoSIfHx8uuugiLrroItM2nU5Hbm6u2SzcvHnzqKysBMDf39+0mG9TgIuOjsbe3v6c63nkkUeIi4vjxhtvPOd9tZc33niDrKwsNm/ebDWn99Tojeyuamj18dtWdbiwpigK2woK6NX7fBZnHcEI2AHv/vcmdvy5AYCAqATuXP696TU6IxRX69lbrTc9P87HiRp9Mz8Z/xMbG0tmZmZbvhUhhBBnwcnJyRTCmiiKwu7du80C3Mcff8y8efMAcHV1tWijJiUlnVEb9aeffuKnn35i1apVNnOKzKFDh3jssce45ZZb6Nevn6q1KIrC/ho9Ww/VkleuM43HJx+JTz5+9/Z3JcjNocPMutnGT9MZeCe/AoMCR+qNpi/YCFz18PPoamvY9NFbFG39o9nXHv/83HIdhUd1GI81sL2ynqguzmbPVRSlw/wQCCFER6fRaAgNDSU0NJQxY8aYth85coSMjAxTgPvzzz9ZunQper0ejUZDVFSU2W21tFqt6dSY4ymKwiOPPMJ5551ntn9rN3PmTADVbylVUFHPxpIayuoMaICmKx9PFdSOd+L4nVOuw9/FnsFBbhbjty2yubDm5eVlmspuUqs38vOOEpw9PDlcZ8CvZzgHi7abv86/8T8uNy+fFh2n6QdFZ1T4fGcVcT71XBrsgev/plfz8vIICws7tzcjhBBCVb6+vlx88cVcfPHFpm319fUWbdTnnnuOo0ePAtC9e3eL8+AyMjLYsmUL69evt5lf5Lds2cLbb7/NokWL8Pf3V6WGWr2RtXurySvX0fSpnesSFU2vL6szNDt+2yKbC2uxsbGsXr3a9PeCinpWF1fz919/498rEgVIGfF/rF0yl/35mQTFJrfKcfPLdRQdLefyEA/2bv2drKws7r///lbZtxBCCOvh7OxMamqq2QVqiqJQVFRktpTIhx9+yPPPPw80ztx16dKFjz76iPz8fNPVqG5ubmq9jVMyGo1MmzaNxMRE7rzzTlVqaBq/6wyN8aq11xFr2t/x43e0t23OstncOmtFRUXEx8czadIkzhs3kcwqDTv+3MD3L81m/FOvknTJVTTU1/HOneM4ULiN4XdMJzR1AK5e3pTtLuTH15/HqNdz94frTnusT2dPo/rwIa5+4mWMRiPVhw9SsOlnflv+MkMvHsKXX37ZKiekCiGEsE2HDx/m2WefZcGCBYwaNYri4mJyc3MxGAzY2dkRHR1tMQtnDbeeWr58OTfffDMbNmwwuzijPSiKwh8HatlYUtOuxwW4KNCNAd1dbWb2s4nNhTVonLqdOv0R8jIzaNDV4RcSwYU3TiXlsv8zPUevq+f3D98g44dVlBXvRDEa8QkKIXrQMC64fgreAT1Oe5xPZ09j6zcfA2Dn4ICrpzeB0QmkjBjLXbfezJAeHjb3hQshhGg9tbW1REVFceGFF7Jy5UoA6urqLNqo6enpVFVVARAQEGAR4CIjI9vtl//Kykqio6MZNmwYH374Ybscs4miKPxSUsOfB2rb9bjHG9jdlcGBbjY1fttkWNtUWqNKIj/RRYFuDAywziluIYQQbe+FF17g0UcfJS8vj8jIyJM+z2g0smvXLosAt3fvXgDc3NxITk42W04kMTGxTdqo999/P2+99Rbbtm2jR4/TT1y0Jhm/z47NhbWCinpW7apSuwyTsWGeNtsDF0IIcfYqKioIDw/nmmuu4bXXXjurfZSVlZldjZqenk5eXp6pjRoTE2MxC9etW7ezrjknJ4eUlBSeeeYZZsyYcdb7ORsyfp89mwprtXojb+aWU2s495IrSvby0tWDTvr4/Z/9jndg8Gn342qv4fZ4H5u+ykQIIcSZmzlzJi+99BKFhYUEBga22n7r6urIzs42C3AZGRlUV1cDEBgYaLGcSERExGkXtFUUhWHDhrF3716ysrJwdm6/oNKa43drsaXx26bC2ldFR8kv17XKFSMGvZ7y/cUnfdwnKAT7FixqqKFxAb4rQ71aoSohhBC2oKSkhIiICO677752WaPMaDSyc+dOizbqvn37AHB3dyclJcVsBi4xMRFXV1fTPj799FPGjx/P6tWrGTFiRJvXfLzWHL9biy2N3zYT1qxt+vRE48I9O8TCe0IIIU5v6tSpfPTRR+zcuVPVG58fOnTIrI2alpZGfn4+RqMROzs7YmNj0Wq1xMfHs3DhQvr06cMPP/zQrjXK+H3ubCKsKYrCO/kVHK4zWFUqb6IB/FzsmRzrbVNXlwghhDhzO3bsIC4ujmeffZbp06erXY6F2tpaizbq33//TUNDAwA9evSwOA8uPDz8rO4Lerq7+cj43TpsIqztO9bA+wWVp3+iym6M7kIPd0e1yxBCCNGGrrvuOn755Rd27Nhh1ma0Vjt27CA+Pp7bbruNIUOGmIW4/fv3A+Dh4WHWRk1NTSUhIQEXF5dT7nvo0KHo9Xo++ugjgoKCLB6X8bt12ERY+6boKLlW1us+kQaI93FitA30voUQQpydtLQ0evfuzZtvvsltt92mdjktMnr0aDIzM8nLy7NYCuTAgQMWV6Nu27YNo9GIvb09cXFxFrNwXbt2BUCn0+Hu7o5er8fHx4dPPvmE4cOHm+1fxu/WYfVhrUZvZHHWkRbfzFVNdsC0JF/cbODKEiGEEGfu8ssvZ+fOneTk5ODQgovQ1Pbdd99xxRVX8NlnnzFu3LgWvaampqbZq1FrahrXRwsODkar1RIYGMhbb70FgJ2dHYqi8MQTTzBz5kzs7e1l/G5FVh/W8srr+arIek9MPNGYUE9ifaz7REUhhBBnbsOGDVx88cV8+umnXH311WqXc1r19fUkJibSq1cvfvzxx3M6J8tgMFBYWGi6L2p6ejp//PEHlZWWLc64uDjWrl1Llbu/jN+txOrD2vp9x/j7YK3NJPP+3VwZ0sNd7VKEEEK0IkVRGDhwIHq9nr///tuqT0ZvMnfuXGbNmkVGRgbx8fGtvv/777+fxYsXo9frLR6bMmUK4x+bL+N3K7H6OdySmgab+KIBjMD+GssfWiGEELbtq6++YvPmzec8Q9Ve9uzZw9NPP80999zTJkEN4J9//jEFNY1GQ79+/Rg9ejSXXHIJ/fv3Z+WOShm/W4lVhzVFUSi14g+vOaU1+tNeyiyEEMJ2GAwGHn30UYYPH25xAr21mj59Op6ensyePbvNjnHJJZcQHh7O5ZdfziWXXIKvr6/pMRm/W5dVh7UavYLOVmL5/+iMCjV6BXdH6/uyhRBCnLn33nuPvLw83n33XbVLaZH169fz8ccf8+677+Ll1XZXOD7++OMnfUzG79ZlnZc9/E+D0apPpzspvXWfBiiEEKKF6urqmD17NldffTX9+vVTu5zT0uv13HPPPQwcOJAbbrhBtTpk/G5dVj2zZqPfNQYb+21CCCFE81577TX279/P008/rXYpLbJkyRJycnLYsmXLWd2RoLXI+N26rHpmzc76ZiJbxN6qP1UhhBAtcfToUZ555hkmT55MTEyM2uWc1sGDB5k1axa33347vXv3VrUWGb9bl5WW1cjRRr9tBys8OVEIIcSZmT9/PseOHWPWrFlql9IijzzyCPb29jzzzDNqlyLjdyuz6jaom4MGJzts6iTFhtoaHrpvttm91Wzh3nFCCCH+deDAARYsWMDdd99NcHCw2uWc1ubNm1m6dClLliwx3Q5KTbY4fjvZaXBzsM6wZvWL4n64vYLiahu5/FdRqN6znS8evY38/HzTvdViY2Mt7q3m5+endrVCCCFO4p577uG9995j586dZktSWCOj0ch5552HXq9ny5Yt2Nvbq10SYGPjNxDi4ch1UV3ULqNZVj2zBhDo5sjear1NLKxnp9EwvG8KT+fkUFtba3FvtS+//JJjx44B0KNHD7PwlpqaSlhYmKonhAohhICdO3fy+uuv8+STT1p9UANYunQpW7Zs4bfffrOaoAY2Nn4DQW7WG4msfmatI90b1Gg0WtxbLT09nZKSEgA8PT1JSUkxC3EJCQm4uLi051sQQohO7cYbb+Snn35ix44duLtb5+2HmpSXlxMdHc2IESN4//331S7HTEcav9VmvTHyf3p5OmIHNpPMQzwdT/64nR1RUVFERUUxfvx40/YDBw6QkZFhCm/r1q1jyZIlpjZqXFycRRvVGs5JEEKIjiYzM5MVK1awZMkSqw9qALNmzaKuro558+apXYqFjjR+q83qZ9YAvik6Sm65DmsuVAPE+zgxOrR1VouuqakxtVGbZuIyMzOpqakBoGfPnhYBLiwszCpvkyGEELbiiiuuID8/n7y8PBwdrXfwhsZgmZqayvPPP8+DDz6odjnN6ozjd1uwibC271gD7xdUql3Gad0Y3YUe7m33H7fBYGDHjh1mLdT09HRKS0sB8PLyaraN6uxsndO6QghhTX799VcGDx7MypUrmTBhgtrlnJKiKAwZMoSDBw+SkZGBk5OT2iU1S8bv1mETYU1RFJbmV1BWZ7DKdK4B/FzsmRzrrcrMVmlpqamN2jQLV1BQgKIoODg4mNqoqampaLVaUlJSbOKkWSGEaC+KonDhhRdSU1Oj+ur/LbFy5Uquu+461q5dyyWXXKJ2OScl43frsImwBrC9sp7Pd1rviYrjwj2J6mI9M1jHjh0jKyvLbAYuMzOT2tpaAEJCQizaqKGhoVb9wyqEEG3l22+/ZfTo0fzwww9cdtllapdzStXV1cTExDBgwAA+//xztcs5LRm/z53NhDWAr4qOkm9lvW8NEOfjxJVW3OtuYjAY2L59uym8paWlkZaWxqFDhwDo0qWLWRs1NTWV+Ph4q51eF0KI1mAwGEzrX/78889W/0vrww8/zKJFi8jLyyM0NFTtclpExu9zY1NhrVZv5M3ccmoN1lOyq72G2+N9cHWw7inzk1EUhdLSUovz4LZv346iKDg6OhIfH282A5eSkoKPj4/apQshRKt4//33mThxIn/88QcDBgxQu5xTKigoIDExkccee8xmboMFpx6/P509ja3ffAyAnYMDbl4+BETFk3LZ/9H7ymvPqCW9/p2X+PG157j0rkcZcvO9p3yuLY3fNhXWAAoq6lm1y3qmU8eGeRLtbd3Tp2ejurq62TZqXV0dAL169bJoo/bq1cvqfyMVQojj1dfXExsbS2pqKqtWrVK7nFNSFIWRI0eSn59Pbm6uzd3K8GTj96ezp1F9+BBXP/EyRqOR6sMHKdj0MxuWLSI0dQATX/oAe4eWrTQ2/6r+JA4fTfa6b3nwy82nfK4tjd9Wv87aiaK9nRkcaGBjSY3apXBRoJvNfNFnysPDg4EDBzJw4EDTNr1eT0FBgVmAe/XVVykrKwPA29vbIsDFxcVJG1UIYbXeeOMNiouL+e6779Qu5bS++eYbfvjhB7744gubC2pw6vHbwckZT7/uAHTpFkiPuBRCkvry9pSxbP1mJf3+78bT7n/nP7/TUF/HJVMeJu3bT9j1zybC+pzf7HNtbfy2uZk1aPztYmNJDX8cqFWthoHdXRkc6NbpZ5IURaGkpKTZNiqAo6MjCQkJFm1Ub29vdQsXQnR6VVVVREREcMUVV7B06VK1yzmluro64uPjiY6OZvXq1TY79jQ3fn86exp1VUe5ccF7Fs9/ecIQPP0DuPmVj067708evwsPX39G3v8E3780m+ryMsbPedXiebY4ftvczBqARqNhcKAbTnYaflFhhu2iQDcGBri1+3GtkUajISgoiKCgIEaOHGnaXlVVRWZmplmAW7lyJfX19QCEhoaaXcig1Wrp2bOnTf3HI4SwbS+99BJHjx7liSeeULuU03rhhRfYu3evTQc1OPPx2z80itLtuad9Xl11FdnrvuXO5Y0zpNqRV/P6zVdw5UPP4eLhaXqerY7fNhnWoPELHxjgRlcXe1YXV1NnUNr0KhMN4GKv4fIQD5uaOlWLp6cngwYNYtCgQaZter2ebdu2mQW4V155hcOHDwPg4+PTbBvV2lcRF0LYnkOHDjF//nzuuusuQkJC1C7nlHbv3s3cuXO5//77iYmJUbucc3bi+H0qiqJAC8Jpxg+f4xvci8DoRACCYpLwDe5F5povOG/cRJsfv22yDXqiWr2RtXurySvXoYFWDW1N+4v3ceKSYA+buGrEliiKwv79+80W9E1PT6ewsBAAJyenZtuoXbp0UblyIYQtu//++1m6dCmFhYX4+fmpXc4pXX311WzatIlt27bh6el5+hfYkFq9kVETbuTA4XImLnjPYvxeNP4iugT0YNLLH55yP6/eeCn7ctPRHHflqGI00iMhlVe/WW/z47fNzqwdz9XBjqtCvYjzrufXkhoO1RnOObQ1vd7PxZ7BQW5Wv2CerdJoNPTo0YMePXowatQo0/ajR49atFFXrFiBTqcDIDw83GIWLjg42KbbA0KI9rF7926WLFnC448/bvVB7aeffuLzzz9nxYoVHS6oQeP4HeLhiF2tA34u9mbjd+Ffv1K6I5dB199xyn2Ubs9lX246t735Ja5dfEyvd6mvZt7EUYRXF+PqkNgO76btdIiZteMpisL+Gj1bD9WSV67DCNgBxha8tul5dhqI93amt78LgW4OEgCsRENDA9u2bTObgUtPT+fIkSMA+Pr6WgS42NhYaaMKIczcfPPNfP/99xQWFuLh4aF2OSfV0NBASkoKfn5+/PLLLx12LJo0aRIHDhxg6dKl7Kuu57dtxfzwwxrWL1tERJ9B3LDgPezs7U/6+m9emMmerH+Y+t4PFuP3BRdcQP/+/XnppZfa8R21vg4X1o5XozdSXNVASY2ekho9pTUN6JpJbU52EODmSJCbAwFuDoR4OuJmw9OlnYmiKOzdu9fiatSdO3cCjW3UxMRE00UMWq2W5ORkvLysf8VqIUTry8nJITk5mUWLFjFt2jS1yzmlBQsWMH36dLZu3UpKSora5bSZSZMm8e677wLg4OCAj48PicnJDB8znvOuupYDdcaTjt92Bh1PXZLE9Xc9wH3/fdBi/F6wYAFz585l3759Nr2MVIcOaydSFIUavYJeUTAYwd4OHDQa3Bw0HfY3ls6qsrLSrI2alpZGTk6OqY0aERFhMQvXo0cP+TkQooMbM2YMmZmZ5OfnW/XgXVJSQkxMDBMnTmTx4sVql6O6zj5+d6qwJjo3nU5Hfn6+xSxceXk5AF27drVYTiQmJgaHFq6cLYSwbn/88Qfnn38+H3zwAddff73a5ZzSTTfdxHfffUdBQQG+vr5qlyNUJmFNdGqKorBnzx6LALdr1y4AnJ2dSUpKMpuBS05O7pAn+grRkSmKwpAhQ6ioqCAtLe2M7jfZ3jZt2sSgQYN48803ue2229QuR3UrVqzgjjuav8igV69e5OTktHNF7U/CmhDNqKioICMjwyzA5eTk0NDQAEBkZKRZgEtNTSUwMLBTTMcLYYtWr17NyJEj+fbbb82uPLc2BoOBfv36YWdnx+bNm7E/xYn1nUVVVRUHDhxo9jFHR0d69erVzhW1PwlrQrSQTqcjLy/PYhauoqICAH9/f4vz4KKjo6WNKoTKjEYjvXv3xsvLy+qvqnzjjTeYMmUKf/zxBwMGDFC7HGElJKwJcQ4URWH37t0WAW737t0AuLi4NNtGteblAoToaFauXMl1113Hb7/9ZnZXFWtz+PBhoqOjufLKK1m2bJna5QgrImFNiDZQXl7ebBtVr9ej0WiIioqymIULCAiw6t/4hbBFOp2OuLg4EhIS+Prrr9Uu55SmTp3KihUrKCgooHv37mqXI6yIhDUh2kl9fT25ubkWs3BHjx4FoFu3bs22UeWcFSHO3pIlS5g2bRqZmZkkJlrvKvZpaWn06dOHBQsWcN9996ldjrAyEtaEUJGiKBQVFVkEuOLiYgBcXV1JSkoyW9Q3KSkJd3d3lSsXwvodO3aMiIgILrvsMtOiq9ZIURQuuOACKioqSE9Pl7uuCAsS1oSwQkeOHCEjI8Ps1lq5ubkYDAY0Gg3R0dHNtlGFEP965plnePLJJykoKCA0NFTtck7q/fffZ+LEiaxbt46hQ4eqXY6wQhLWhLARdXV1zbZRq6qqAOjevbvFciKRkZHSRhWd0uHDhwkPD2fSpEksWrRI7XJO6ujRo8TExHDhhRfyySefqF2OsFIS1oSwYUaj0ayN2jQTt3fvXgDc3NxITk42C3FJSUm4ubmpXLkQbWv69Om8/vrrFBYW0q1bN7XLOanp06ezZMkS8vLyCAkJUbscYaUkrAnRAZWVlVlcjZqXl4fBYMDOzq7ZNqpcfSY6ij179hAVFcXDDz/ME088oXY5J5WXl0dycjJPPPEEM2fOVLscYcUkrAnRSdTV1ZGTk2M2C5eRkUF1dTUAgYGBFgEuMjLSqm/LI0Rzbr31Vr766isKCwvx8vJSu5xmKYrCpZdeyq5du8jOzsbFxUXtkoQVk7AmRCdmNBrZuXOnxXlw+/btA8Dd3d2ijZqYmChtVGG18vPzSUhIYMGCBdx7771ql3NSq1atYty4cXzzzTdcccUVapcjrJyENSGEhUOHDpm1UdPS0sjPz8doNGJnZ0dMTIzZciJarRZ/f3+1yxaCq6++mr///puCggKcnZ3VLqdZNTU1xMfHk5iYyLfffqt2OcIGSFgTQrRIbW0t2dnZZjNwGRkZHDt2DICgoCCLNmpERIS0UUW7+euvvzjvvPNYvnw5N910k9rlnNTs2bN57rnnyMnJITIyUu1yhA2QsCaEOGtGo5HCwkKLNur+/fsB8PDwMLVRm2biEhIScHV1Vbly0dEoisKwYcM4ePAgGRkZVrtkza5du4iLi+OBBx7g2WefVbscYSMkrAkhWt2BAwcsrkbdtm0bRqMRe3t7YmNjLWbh/Pz81C5b2LAff/yRSy+9lK+++oorr7xS7XJO6v/+7//YsmUL+fn5cicS0WIS1oQQ7aKmpqbZNmpNTQ0APXr0sFjUNywsTNqo4rSMRiP9+vXD2dmZ33//HY1Go3ZJzVqzZg0jRozgo48+4pprrlG7HGFDJKwJIVRjMBgoLCw0u61Weno6paWlAHh6epKSDjhhAAABAABJREFUkmIW4hISEmSZA2Hmk08+4ZprruGXX35h8ODBapfTLJ1OR1JSEkFBQfz8889WGyiFdZKwJoSwOqWlpc22URVFwd7enri4OIs2ateuXdUuW6igoaGBhIQEoqKi+O6779Qu56TmzZvHo48+Snp6OomJiWqXI2yMhDUhhE04duwY2dnZZrNwmZmZ1NbWAtCzZ0+LABcWFiYzGB3cG2+8wZQpU0hPTyclJUXtcpq1b98+YmNjueWWW1i4cKHa5QgbJGFNCGGzDAYD27dvN5uBS0tL4+DBgwB4eXk120a11vW3xJmpqakhMjKSiy++mBUrVqhdzkldf/31/PjjjxQUFODt7a12OcIGSVgTQnQ4paWlFje33759O4qi4ODgYGqjNi0nkpKSgq+vr9plizP0/PPP89hjj7Ft2zbCw8PVLqdZGzdu5KKLLuKdd95h8uTJapcjbJSENSFEp1BdXU1WVpbZLFxmZiZ1dXUAhISEWLRRQ0NDpY1qpcrLywkPD+f6669n8eLFapfTLL1eT58+fXBxceGPP/6QK5vFWZOwJoTotPR6fbNt1EOHDgHQpUsXUxu1aRYuPj4eJycnlSsXDz/8MK+88gqFhYUEBASoXU6zXn31Ve6++242b95Mv3791C5H2DAJa0IIcRxFUSgpKbG4K8P27dsBcHR0JD4+3mwGLiUlBR8fH5Ur7zz27dtHZGQkDz74IE899ZTa5TTr0KFDREdHc/XVV/PWW2+pXY6wcRLWhBCiBaqqqizaqFlZWaY2aq9evcwW9NVqtYSEhEgbtQ1MmTKFTz/9lJ07d9KlSxe1y2nW7bffzqeffkpBQQH+/v5qlyNsnIQ1IYQ4S3q9noKCArMLGdLT0ykrKwPA29vb4jy4uLg4aaOeg4KCAuLj43n++ef573//q3Y5zdqyZQv9+/fn5ZdfZtq0aWqXIzoACWtCCNGKFEVh//79Fm3UHTt2AI1t1ISEBIsQZ60zRNbmmmuuYdOmTWzfvt0q72RhNBo5//zzqampYevWrTg4OKhdkugAJKwJIUQ7qKqqIjMz0+xChuzsbOrr6wEICwuzCHA9e/aUNupx/vnnH/r27WvVy2AsX76cm2++2apvfSVsj4Q1IYRQSUNDA9u2bbO4GvXIkSMA+Pj4NNtGdXR0VLlydVx22WUUFxeTlZVllTNWlZWVREdHM2zYMD788EO1yxEdiIQ1IYSwIoqisG/fPos2amFhIQBOTk4kJiaaBbjk5OQO30b9+eefGTZsGJ9//jljx45Vu5xm3X///bz11lts27aNHj16qF2O6EAkrAkhhA2orKw0a6Omp6eTnZ2NTqcDIDw83GIWLjg4uEO0URVF4bzzzkOj0fDnn39a5XvKzs5Gq9XyzDPPMGPGDLXLER2MhDUhhLBRDQ0N5OfnW7RRy8vLAfD19TVbSkSr1RITE2NzbdRVq1Yxbtw4fv75Zy6++GK1y7GgKArDhg1j7969ZGVlyb1nRauTsCaEEB2Ioijs3bvXYjmRXbt2AeDs7NxsG9XLy0vlypun1+tJTEykV69erFmzRu1ymvXpp58yfvx4Vq9ezYgRI9QuR3RAEtaEEKITqKioaLaN2tDQAEBERITFor5BQUGqtxzfeecdbr31Vv755x969+6tai3NOXbsGLGxsfTu3ZuvvvpK7XJEByVhTQghOimdTmfWRm2aiauoqADAz8/P4jy4mJiYdrsSs7a2lujoaAYNGsRHH33ULsc8U4899hjz588nNzeX8PBwtcsRHZSENSGEECaKolBcXGxxNWpRURHQ2EZNSkqyaKN6enq2ei3z58/n4YcfJi8vj6ioqFbf/7nasWMHCQkJzJgxgzlz5qhdjujAJKwJIYQ4rfLycos2ak5ODg0NDWg0GiIjIy1m4QIDA8+6jVpRUUF4eDjjx4/n9ddfb+V30zpGjx5NZmYmeXl5uLm5qV2O6MAkrAkhhDgrOp2O3Nxci1m4yspKAPz9/c3CW2pqKtHR0djb259234899hgLFixgx44dBAUFtfVbOWPfffcdV1xxBZ999hnjxo1TuxzRwUlYE0II0WoURWH37t0WAW737t0AuLq6WrRRk5KS8PDwMO2jtLSUiIgI7rnnHubOnavWWzmp+vp60xWqP/74o+oXYYiOT8KaEEKINnfkyBEyMjLMAlxubi56vR6NRkNUVJQpvP3+++/8+uuvFBUV4ePjo3bpFubOncusWbPIyMggPj5e7XJEJyBhTQghhCrq6+st2qhbt26luroagO7du1ucBxcVFdWiNmpb2bNnD7GxsUyZMoUXX3xRtTpE5yJhTQghhNW47rrrWLduHYsWLSIvL88U4oqLi4HGNmpycrJFG9Xd3b1d6pswYQIbNmygoKDAahcSFh2PXVvufMiQIWg0Gov1cRYuXEhoaGhbHvqUQkND0Wg0Zn+Cg4PP+PE///zTbL/33XcfQ4YMaa+3wfLly/H29m6TfW/YsAGNRmNab0kIIdpaeno6K1eu5KmnnmLChAk8+eSTfPXVV+zevZuysjLWrVvH008/TXR0NJs2bWLatGkMHDgQT09PYmNjmTBhAs8//zxr1qzhwIEDrV7f+vXr+fjjj5k3b54ENdGu2mRlQ51Oh5OTEwAuLi489thjjBs3rlXvR9fQ0HDG+1MUhRq9glGBR2Y9wU2Tb8XRToOrg8Zikcc5c+Zw2223mf5+4rS7i4sLM2bM4Jdffjn7NyGEEMLk0UcfJSoqiptvvtnisa5duzJ06FCGDh1q2lZXV2fRRv3++++pqqoCICAgwKKNGhkZeVZt1IaGBu655x7OP/98brjhhrN/k+KsNI3fDcbGMdxOA452GtwcNJ3iAo8Wz6xdc801+Pj40LVrV6666irTAokAkyZNYsyYMcydO5egoCCio6NNj1177bVUVlby1ltvnXL/r732GhERETg5ORETE8P7779v9rhGo+H111/nqquuwt3dnaeffponnngCrVbL0qVLCQkJwcPDgzvvvBODwcC8efPoHhBAV/9u3DJjNh9ur+ClzMO8kn2EqgYjmcfs+eKIM5+UObHyoCM/Vjiyft8x8srrUQBPT08CAgJMf/z9/c3queOOO/jzzz/5/vvvW/oRmjEajcyZM4fg4GCcnZ3RarX88MMPpsebm9lKT09Ho9FQVFTEhg0buPnmm6msrDTN/j3xxBNA48zfU089xXXXXYeHhwdBQUG88sorpv0UFRWh0WhIT083bauoqECj0bBhwwaKiopMN0v28fFBo9EwadKks3qfQgjREr/88gurV6/mmWeeafEv4i4uLvTu3ZvJkyfz8ssvs3HjRioqKigsLOSzzz7j1ltvxcHBgQ8++IAJEyYQGxuLl5cXAwcO5M477+SNN97gr7/+oqam5rTHWrJkCTk5ObzyyivY2bVpU0oANXojeeX1rN93zGz8fj23nDfzynk9t5xXso/wUuZhPtxeYRq/a/RGtUtvEy2eWfPw8GDjxo04ODjw9NNPM2LECDIzM00zaOvWrcPLy4sff/yR40+D8/Ly4tFHH2XOnDncdNNNzZ5X8MUXX3DvvfeycOFChg8fzrfffsvNN99McHCwKTQAzJ49m7lz5/LSSy9hb2/PsmXLKCwsZPXq1fzwww8UFhZy9dVXk7djJ549wpj42pfsyviLpU/ei3P8QEKS+zb73nRGKK7Ws7dajxGo0hnJPlLHvmMNBLk5NJvaQ0NDmTJlCo888ggjRow44/94Fy1axIsvvsgbb7xBamoqS5cu5corryQnJ6dFK3Wff/75LFy4kFmzZrFt2zYAs0vfX3jhBR599FGeeOIJ1qxZw/33309sbCyXXHLJaffds2dPPv/8c8aNG8e2bdvw8vLC1dX1jN6fEEK0lKIoPPzww/Tp0+ec1yyzs7MjPDyc8PBws32VlZWZXY3622+/8dZbb2EwGLCzsyMmJsZiFq5bt24AHDhwgFmzZnH77bdb5f1JOwpFUdhfo2froVryynUYaZxROlX8OnH8tgPifJzo7e960vHbFrU4rL399tumN71s2TK8vb3ZsGEDl156KQDu7u68/fbbpvB2vKlTp7Jo0SIWLFjA448/bvH4/PnzmTRpElOnTgXggQce4M8//2T+/PlmYe26665j8uTJZq81Go0sXboUT09PHIIiiOp/AZm5+Ux/7n00dnZ0DY1k47uvsPOf383C2g8vP8WPS/5dv+fSaTMZdO3tACjA8udm8/78p9Cgwd4O5j77LPfcc4/ZsR977DGWLVvGihUruPHGG1v6UZre84wZM5gwYQIAzz//POvXr2fhwoW8+uqrp329k5MTXbp0QaPREBAQYPH4oEGDePjhhwGIjo7m999/56WXXmpRWLO3t8fX1xeAbt26tdl5cUIIAfD111/z559/snbt2jabtfLz82PYsGEMGzbMtK2uro6cnByz+6J+8803pqtRAwMD0Wq17Nu3D4PBwM0334zRaJSZtTZQUFHPxpIayuoMaGgch+HUQe14xuP+N7dcR065Dn8XewYHuRHVxbnV621vLQ5rJ973ra6ujsLCQtPfk5KSmg1q0HgvuTlz5jBt2jTuvPNOi8fz8vK4/fbbzbYNGjSIRYsWmW3r29dyZiw0NBQHV3e+KjpKXrkOZ28/uoXboTnuPyYPX3+qj5SZvW7wxLvoPXqC6e/uPl2bfbzph6ZrWAC1eiOuDv/u19/fnwcffJBZs2ZxzTXXNPvem3P06FH279/PoEGDLN5zRkZGi/dzKgMHDrT4+8KFC1tl30II0VoMBgOPPvoow4YNa9Evk63JxcWFPn360KdPH9M2o9HIzp07TTNwGzZsIDMzE4ABAwbg7u5OSkqK2QxcYmKidB/OUq3eyNq91eSV62iaAzvXJSqaXl9WZ+DznVXE+dRzabCH2fhta1oc1o4/v6nJ8edxne6y6RtuuIH58+fz9NNPN3sl6IlTlYqiWGxr7hhGOwfezC2nzqA07Qh7hxPOd9BoUIzm+dzN2xe/kPCT1nvi43sN8GZuOZeHeJg974EHHmDJkiUsWbLkpPs6mVO956bf3I5vKTc0NJzxMZo7XlvsWwghzsYHH3xAbm4uy5cvV7sUoPHfx8jISCIjIxk7diznnXceWq2W1atXk52dbRbiXn/9ddNMW2xsrEUb9cRznYW5gop6VhdXm8bv1l5HrGl/+eU6io42jt/R3rY5y9bisNatW7dzulTZzs6OuXPnMnbsWIvZtbi4OH777TcmTpxo2rZp0ybi4uJOuj9FUdhT3UClzkCtoe2XilOAWoPCql1V1BsUU9Dx8PDg8ccf54knnmD06NEt2peXlxdBQUH89ttvDB482LR906ZN9O/fH/g3CJeUlJhW8D4xMDs5OWEwGJo9xonLivz555/ExsZa7Ds1NfWk+wZOun8hhDhXdXV1zJo1i3HjxtGvXz+1y7GwdOlStmzZwm+//Wa62Gz48OGmx2tra80CXHp6Ol999RXHjh0DICgoiNTUVLMAFx4e3unbqIqi8MeBWjaWnP7CjlY5Hv+O3xcFGhjQ3dXmzmVrcVi76qqrTFcvFhcXs2rVKqZPn262/tjpjBo16v/Zu+/4pqv1geOfdEELXUCBMttSOulgyJK9ZO+NQIoXFC8ICgIKiiD4AxRRQPEq0LD33sgQlCnQ0pYuKJRVNi3dI8n390dvc42pzMxy3q8Xr+vN+J4nTdPz5Dxn0KhRI/7zn/9QqVIlze0ff/wx/fv3p169erRt25Zdu3axdetWDh06VOx1JEni2J1srmeYZjQoVyWRlF6gGQkbNWoUCxYsYN26dTRq1Oi5rvHxxx8zffp0atWqRWhoKOHh4URGRrJmzRoAvL29qV69Ol988QWzZs3i8uXLOrtle3h4kJmZyeHDhwkJCcHBwQEHBwcATpw4wbx58+jZsye//vormzZtYs+ePUDhppKNGzdmzpw5eHh48PDhQ6ZNm6Z17Zo1ayKTydi9ezedO3fG3t5eawGDIAjCq/rpp5+4ffs2Bw8eNHUoOlJTU/nkk08YOnSozpSVIvb29rzxxhtaiaZarSYpKUkrgQsPDyclJQUo/IJfXBm1dOnSRnldplbUf5++l2OS9o/dySZfLdHC3cGiErbnTtZq1KhB7969ycjIoGrVqrRt2/alRtrmzp1L06ZNtW7r2bMn33//PV9//TUffPABnp6ehIeH/+MGs6fu5ZjsjS6SnJHP6Xs5NKnsgK2trWarjOf1wQcfkJ6ezoQJE7h//z4BAQHs3LlTsxLU1taWdevWMXr0aEJCQnjjjTeYNWsW/fr101yjadOmvPfeewwYMIBHjx4xffp0zfYdEyZM4Pz588yYMQNHR0fmz5/PW2+9pXnu8uXLGTFiBA0aNMDX15d58+ZpFosAVK1alRkzZjBlyhTCwsIYNmyY2ZQpBEGwfOnp6cyePZuwsDB8fX1NHY6Ozz//nLy8PObOnftCz7OysqJ27drUrl1b6+/1vXv3tFajHjlyhCVLlqBWq7G2tsbf318rgQsJCaFChQr6flkmZw7996l7OdhZyWhS2cGkcbwIiztuKjEtj63XMkwdhkZvT0ezq4F7eHgwfvx4xo8fb+pQBEEQijV9+nTmzp3LlStXXqhCYwxRUVHUrVuXefPmMWHCBIO1k52drVNGvXjxombft2rVqunMg/P09LTYMqrov1+eQU4wMJQcpZp9NzJNHYaWfTcyqV7W1qJXmQiCIBjT/fv3mT9/PmPHjjW7RE2SJMaMGYOPjw9jx441aFsODg40bNhQM1cZCucJJyUlabYSiYyMZOnSpdy9exco3JkhJCREay5cYGAgpUqZd9Ih+u9XY1HJ2sFb/1s1Yi5yVRK/3sqku4d2Sfhp87v27dtH8+bNDR2aIAiCWZo9ezY2NjZ88sknpg5Fx/r16/n99985ePDgP25HZUjW1tb4+Pjg4+OjtSXU3bt3tcqov/76K4sXL0aSJGxsbIoto5YvX/4pLRmXJfXf5shiyqDmNnz6d328HLU23rty5co/PrZq1apiTx5BEF5L165dw9fXly+++IJPP/3U1OFoyczMxNfXl8aNG7NlyxZTh/NMWVlZWmXUiIgIoqKiyMkpnBNWvXp1TfJWNBLn4eFh9In1ltZ/myOLSNYkSWJZfBqPclV634dFH2RAhdLWjPBzsajVJYIgCMY2bNgwfv31V65cufLM/TmNbcqUKXz//ffExcUVux+oJVCpVFy+fFlrHlxERAT3798HCreO+vs8uICAAIOVUUX/rR8WkazdzipgVeITU4fxTEN9nKla5vkOIBYEQXjdREdHExISwg8//FDsaTamlJiYSJ06dZg2bRqff/65qcPRu7t372olcJGRkSQmJmrKqAEBATpl1KJjB59mxIgRyGQyFi5cWGzyLfpv/bCIZG1XcjqxqflmmZUXkQEBrnZ0s4DatyAIgil0796d2NhY4uLisLU1n45RkiQ6d+5MfHw8sbGxr800lczMTKKjo7USuKioKHJzc4HC/Tb/PgpXtAcnFI7ilSlThry8PHx8fNi+fbvOZvai/9YPs19gkK1UE2fmbzQU7pAcl5pP22pqHCxgZYkgCIIx/fHHH+zatYu1a9eaVaIGsGvXLvbv38/27dtfm0QNChfCNWnSROssaaVSqVNG/fHHH3nw4AEAzs7OmsStUqVK5OXlAZCUlES9evVYunQpQ4YMAUT/rU9mP7IWl5rHjmTznZj4dz09HPFzNe+JioIgCMYkSRLNmzcnKyuL8+fPm9U+YTk5OQQGBuLj48O+ffvMet6SqUiSpFVGLdpW5PLly8U+vmvXrqxfv54b+Tai/9YTsx9Zu5utxApQP/ORpmdFYbzm+mYLgiCYwt69ezlx4gT79u0zq0QN4JtvvuHWrVsiUXsKmUyGu7s77u7udOrUSXP7hx9+yKJFi3TOkN69ezcffPABb89cKPpvPTH7ZO1OdoFFvNFQ+AuZkq00dRiCIAhmQ6VS8cknn9CyZUutI+/MwfXr1/nqq6/48MMPzfLIK3MXExODSqXC2toalUpF+fLl6dy5M/Xr12fUqFFsu5Un+m89MetkTZIk7prxD684d7OVmgPeBUEQXnfr1q0jOjqaU6dOmd3fxQkTJuDq6sq0adNMHYpF8vT0pFGjRnTr1o1OnToRGhqqGTkt7L+zTBzhizHn/tusk7VspUS+paTl/5WvlshWSpSxNb83WxAEwZjy8/P57LPP6NmzJ40bNzZ1OFoOHTrEli1bWLNmDY6OjqYOxyL9/PPP/3if6L/1y6yTtQK1Wa99+EdK816zIQiCYBQ///wzN27cYPfu3aYORUtBQQEffPABzZs3Z9CgQaYOp0QS/bd+mXWyZqHvNSoL+zYhCIKgb5mZmXz55ZcMGzaMwMBAU4ejZdGiRSQkJLBu3TqzLHmVBKL/1i/zWpbzN1YW+hmyNuufqiAIguEtWLCAtLQ0ZsyYYepQtNy5c4cvvviC0aNHExISYupwSizRf+uXmYZVyNZC320b8U1NEITX2MOHD/n666/597//TY0aNUwdjpYpU6ZQqlQpZs6caepQSjTRf+uXWSdrDjYy7Mw6Ql2qvFx+WvQdR44c4fHjx6YORxAEwei++uorAD799FMTR6Lt5MmTrFy5kq+++uq5zr0UXp4l9t92VjIcbMwzWTP7EwzWXk7jRqZlbN8hSRIPE6NYMqIbOTk5ANSoUUPnbDUPDw8xT0IQhBLpxo0b1K5dm2nTpvHZZ5+ZOhwNlUrFG2+8gZWVFWfOnMHa2trUIZV4ltR/A9Qoa8vg2s6mDqNYZr3AAMDdwZZbmUqL2FjPWiajW/PGzMvI0Dlb7aeffuL+/ftA4dlqISEhWglcQEAApUqZ587JgiAIz+uLL77A2dmZDz/80NShaPnll1+IiIjg1KlTIlEzEkvqv62AKg7mmxKZ/chaSTob9J/OVpMkCVtbWwICArQSuJCQEFxdXY38CgRBEF5ObGwsQUFBfPfdd4wdO9bU4Wg8evQIHx8funfvTnh4uKnDeW2UpP7b1Mw+WctWqlkc/dhiMvMxQeVwsHn+Qn1mZibR0dFao3BRUVHk5uYCULNmTZ0yas2aNUUZVRAEs9OrVy8iIyOJj483q0rB+++/z5o1a0hMTKRSpUqmDue1UdL7b2My3zG//3KwscLf1Y7Y1HzMOauUAf6udi/8RpctW5YmTZrQpEkTzW1KpZLLly9rRt8iIyP54YcfePjwIQAuLi6EhIRQt25dTQLn7++PnZ2dPl+SIAjCczt9+jTbt29n1apVZpWoRURE8NNPP/Htt9+KRM3ISnr/bUxmP7IGcDurgFWJT0wdxjMN9XGmahlbg1xbkiTu3LmjNQJXVEYFsLW1JTAwUKeM6uLiYpB4BEEQikiSROvWrXn8+DERERFmMydMkiSaNWvGkydPiIiIwNbWMH+fhX8m+m/9MPuRNSic9OdW2pqHuSqzzM5lQIXS1gadnCiTyahSpQpVqlShc+fOmtszMjK0yqgRERGsW7eOvLw8ADw8PDTJW9FIXPXq1UUZVRAEvTlw4ADHjh1j165dZpOoAaxevZqTJ09y5MgRkaiZiOi/9cMiRtYALj/JY8tV852o2MfLkdrO5jH0r1QqSUhI0BqBi4iI4NGjRwC4urrqzIPz9/cXf8wEQXhharWa+vXrU7ZsWY4fP242XwTT09Px9fWlRYsWbNiwwdThvNZE//3qLCZZA9iRnE68mdW+i2rd3T2cTB3KU0mSREpKik4Z9cqVKwDY2dkVW0Z1djbPPWcEQTAP69evZ9CgQfz+++80a9bM1OFoTJw4kSVLlhAfH0/16tVNHU6JoFAoGD9+PGlpaS/83Bfpv1NTbjCva33GrjtCFd+gF27ref21/5bJZGzbto2ePXsarL1XYVHJWo5Szc+xqeSozCdke2sZowJcsTfjiYlPk56eTlRUlFYCFxMToymjenl56YzCVatWzWy+PQuCYDr5+fkEBATg7+/Prl27TB2ORlxcHMHBwXzxxRdMnTrV1OGY1MmTJ2nevDnt27dn//79z/08Dw8Pxo8fz/jx4zW35eTkkJGRQcWKFV84jn/qvzdNH0NuRjpDv12puU2tUpGV+hAHl/JY2xiuPPnX/vvu3bu4uroafXFMq1at+O233575OPMu0v6NvY0VnWqUZes18xlO7VSjrMUmagBOTk40a9ZM6xtxQUGBThn1u+++0xyfVa5cOZ0Ezs/PT5RRBeE1s2zZMq5evcq2bdtMHYqGJEl88MEH1KxZkwkTJpg6HJNbvnw5Y8eOZenSpdy4ceOVzmq1t7fH3t7+5Z77Av23lbU1jhUMv3L3r/135cqVDd7eq7C4LMPHpRQt3B1MHQYALd0d8HEx7zr3y7C1taVOnTq8/fbbfPPNNxw6dIiHDx9y8+ZNdu7cyfjx43FxcWH79u0MGzaM4OBgypYtS/369fnXv/7F4sWL+eOPP0hPTzf1SxEEwUCysrKYOXMmb7/9NkFBhitVvaht27Zx6NAhvvvuO0qXLm3qcEwqKyuLjRs3Mnr0aLp27YpCodC6f+fOnTRo0IDSpUtToUIFevfuDRSO9ly/fp0PP/wQmUymqaQoFArNDgMJCQnIZDLi4+O1rvntt9/i4eGBJEmoVCreeecdPD09sbe3p1ujYO7u/t+mxId+mseFXRuI/W0fn9Rz45N6blw9d4LUlBt8Us+NlIRozWOvnj/BD0M7MK1RVb7qEMj+hTNRKf93lNXPI3uwc94n7PtuBjNb1WZ2+wAO/TTvH382f++/ZTIZ27dvByA5ORmZTMbWrVtp3bo1Dg4OhISEcOrUKc3ji34W27dvx8fHh9KlS9O+fXtu3rypeYxcLtcpq44fP55WrVpp7j927Ng/xvhXFjWyVqRJJXsK1BKn7uWYNIbGlV7uG4YlkslkVKtWjWrVqtGtWzfN7U+ePNEqo0ZERLBq1Sry8/MBqFWrls4oXNWqVUUZVRAs3Pfff8+jR4+YMWOGqUPRyM7O5qOPPqJLly507drV1OGY3IYNG/D19cXX15e3336bsWPH8tlnnyGTydizZw+9e/dm6tSpmr/Ze/bsAWDr1q2EhIQwatQoRo4cWey1fX19qV+/PmvWrOHLL7/U3L527VoGDx6MTCZDrVZTrVo1Nm7cSIUKFTh58iSjRo3iY7dKlG7UmebD3uf+tUTysjLo+8VCAOydXcl4cFerrSf376AYO5j63QbQb+YPPEi+zLYvP8LGrjTt3pukedyF3RtoNmQ076/cz42oc2yePpaaoQ2p3biV1vWet/+eOnUq33zzDbVr12bq1KkMGjSIK1euYPPf0mx2djazZ89mxYoV2NnZ8f777zNw4EBOnDjx7DeHws9QYmLicz3WIpM1mUxGC3cH7KxkHLuTbfT2W7o70KSyeYzumZqzszPNmzenefPmmtsKCgqIi4vTKqN+++23pKamAlC+fHmd7UR8fX01HwBBEMzbo0ePmDt3Lu+99x6enp6mDkdj7ty53Llzh0OHDpk6FLOwbNky3n77bQA6duxIZmYmhw8fpl27dsyePZuBAwdqJdshISFA4VQXa2trHB0dn1oeHDJkCIsXL9Yka4mJiZw/f56VKwvnn9na2mpd39PTk5MnTxJzaCcTevbl2B2wLV0aVUH+U8uepzcux6VyFbpPmYtMJqOiZ23SH9xl/8KZtBk1ESurwiKhu3cA7d79GIAKNWpxasMyks4e10rWXqT/njhxIl26dAFgxowZBAYGcuXKFfz8/IDCvm7x4sU0atQIgBUrVuDv78/Zs2dp2LDhM6/v7Oz83JvZW2zvKJPJaFLZgfKlrdl3I5NclWTQVaIyoLS1jE41ypbI0qc+2draEhwcTHBwMMOGDQMK55HcvHlTK4HbunUr8+fPB6BUqVIEBQVpjcAFBwfj6OhoypciCEIx5s6di0qlYtq0aaYORePq1avMnTuXiRMn4u3tbepwTC4hIYGzZ8+ydetWAGxsbBgwYADLly+nXbt2REZG/uOo2fMaOHAgH3/8MadPn6Zx48asWbOG0NBQAgICNI/56aefWLp0KdevXycnJ4f8/HxCQ0M1/ffW56iyPLh2mRpBDbQqMh6hDcnPziL9Xgou7tUAqFw7QOt5jhUqkfn44Uv338HBwZr/dnd3B+D+/fuaZM3GxoYGDRpoHuPn54eLiwtxcXHPlay9CItN1or4uJSiellbDt7KJC41HxnoNWkrup6/qx3tq1n2YgJTkslk1KhRgxo1atC9e3fN7WlpaURFRWmO1jp37hwrVqygoKAAAG9vb60Erm7duri7u4syqiCYyK1bt1i0aBGTJk16qVWBhvLRRx/h5ubGp59+aupQzMKyZctQKpVUrVpVc5skSdja2pKamvrSCwX+yt3dndatW7N27VoaN27MunXrePfddzX3b9y4kQ8//JD58+fTpEkTHB0d+frrrzlz5gxQ2H/7OtuRmFPYr/5T/y0hwd/+5ms2svjL7VY22ovcZDIZkqR+6f77r4vmivoctVr7pNPi+qKi26ysrPj7hhtFfduLsvhkDQpXmfTwcMLfJY/f72TzIFf1yklb0fMrlLamRRUHs98wz1K5uLjQokULWrRoobktPz9fp4z6zTffaPb2cXNz05kH5+PjI8qogmAEM2bMoGzZsma10nL//v3s2LGDDRs2UKZMGVOHY3JKpZKVK1cyf/58OnTooHVfnz59WLNmDcHBwRw+fJiwsLBir2FnZ4dKpXpmW0OGDGHy5MkMGjSIpKQkBg4cqLnv999/p2nTprz//vua25KSkrSeb1+6FBVLyejt6ajpv/+uoqcPl47sRpIkTSJ0/eKflCpTFqeK7jqPL+q/7axk1Cxra7B9UJVKJefOndOMoiUkJJCWlqYZeXNzcyMmJkbrOZGRkVpJYIkvgxbHx6UUtZ3tSMlWcuFBDnGp+agpXPKqftaT//I4KxkEuJSinltp3B1sxCiOkdnZ2RESEkJISAjDhw8HCr9F3bhxQ2shw8aNG/n6668BKF26dLFl1LJly5rypQhCiRIfH8/y5cuZP38+Tk7msRF4fn4+48aNo3Xr1vTr18/U4ZiF3bt3k5qayjvvvKOzsXnfvn1ZtmwZCxYsoG3bttSqVYuBAweiVCrZt28fkyYVTtj38PDg+PHjDBw4kFKlSlGhQoVi2+rduzejR49m9OjRtG7dWmskz9vbm5UrV3LgwAE8PT1ZtWoVf/75p9Y8Rw8PDw4cOIB0L5nuFcqRZVuGQ1n/S2asgMb9R3Bi7c/snDuFJgP+xcPrVzj80zyaDRmtma9W1Ev/tf8+7WiLk53hjj+ztbVl7NixLFy4EFtbW8aMGUPjxo01yVubNm34+uuvWblyJU2aNGH16tXExMRQt25drdf/PEpUsgaFw49Vy9hStYwtbaupuZFRwJ1sJXeyldzNLiC/mKzNzgoqO9hSxcGGyg421HC0xUGUO82KTCajZs2a1KxZkx49emhuT01N5eLFi5ok7uzZs4SHh6NUKpHJZNSuXVtnFK5y5coiAReEl/DZZ59RrVo13nvvPVOHovHdd9+RlJTEli1bxOf6v5YtW0a7du2KPYGmT58+fPXVVzg5ObFp0ya+/PJL5syZg5OTk1aFY+bMmbz77rvUqlWLvLw8nXJeEScnJ7p168amTZtYvny51n3vvfcekZGRDBgwAJlMxqBBg3j//ffZt2+f5jEjR47kt99+o0GDBmRmZnL06FHa/TeBaV2lDC4V7blTtgYjF69l54IZLBzYCgdnFxr0HEzrf32k6b8dba2o5WTHmDrljNZ/Ozg4MHnyZAYPHsytW7do1qyZ1s/grbfe4rPPPmPSpEnk5uYyYsQIhg0bRnT0/7YkmThx4nO1ZVEnGLwqSZLIVkooJQmVGqytwEYmw8FGJj7kJUheXp5OGTUyMpInT54AULFixWLLqOZ0ALUgmJs///yThg0bEh4ejlwuN3U4ANy+fRs/Pz/eeecdvvvuO1OHIxiQufXfr3L01st4rZI14fUlSRLJyck6CdyNGzeAwp25g4KCNFuJhIaGEhQUJOa/CMJ/tWvXjjt37hAVFWU2X2yGDBnCr7/+SmJiomazVkEwBmMnayWuDCoIxZHJZHh6euLp6UmvXr00tz9+/FirjHry5EnNKiqZTIaPj0+xZVRBeJ0cOnSIw4cPs337drNJ1I4fP87atWtZvny5SNSEp1qzZo3WKtW/qlmzJpcuXTJyRC9OjKwJwt/k5uYSGxurMwqXkVF4pl2lSpV0thPx9vY2m05MEPRJrVbTsGFDbG1tOXnypFlMGVEqldSvXx97e3tOnjypmWQuCMXJyMjg3r17xd5na2tLzZo1jRzRixPJmiA8B7VaXWwZtegcOAcHB4KDg7WSuKCgIBwcxEkXgmXbtGkT/fv357fffqNly5amDgeAH374gbFjx3LmzBneeOMNU4cjCAYnkjVBeAUPHz7UKqNGRkYSFxeHSqXCysqq2DJqpUr/fKyKIJiTgoICAgMD8fb2Zu/evaYOB4AHDx7g4+ND3759+eWXX0wdjiAYhUjWBEHPcnNzuXTpks4oXGZmJlC46/ffEzhvb29RyhHMzi+//MKoUaOIiIggNDTU1OEAMGrUKDZt2kRiYiJubm6mDkcQjEIka4JgBGq1mmvXrmk29C1K4G7fvg1AmTJldMqoderUEWVUwWSys7OpXbs2LVu2ZO3ataYOB0CzW/zChQsZM2aMqcMRBKMRyZogmNCDBw90yqjx8fGaMqqvr6/WdiKhoaFiNEEwinnz5jF16lTi4+OpVauWqcNBrVbTtGlTsrOzuXDhgjheTnitiGRNEMxMTk6OVhk1IiKCixcvkpWVBUCVKlV0yqi1atUSZVRBb1JTU/Hy8mLw4MH88MMPpg4HKNzXKiwsjGPHjmnttC8IrwORrAmCBVCr1SQlJenMg0tJSQGgbNmymjJq0UhcYGAg9vb2Jo5csESffPIJCxcu5MqVK7i76x6UbWxPnjzBx8eHtm3bmk1JVhCMSSRrgmDB7t+/rymjFs2FS0hIQK1WY21tjZ+fn84o3D8dyCwIACkpKXh7e/Phhx8ye/ZsU4cDwIcffsgvv/xCQkKC1kHhgvC6EMmaIJQw2dnZxMTEaI3AXbx4kezsbACqVq2qs6mvp6enKKMKAIwePZqNGzdy9erVYg8CN7aYmBhCQ0OZPXs2kydPNnU4gmASIlkThNeASqXSKaNGRERw9+5dABwdHQkJCdFK4gIDAyldurSJIxeM6fLly/j7+zNnzhwmTpxo6nCQJIm2bdty+/ZtoqKiKFWqlKlDEgSTEMmaILzG7t69q7MaNSEhAUmSsLa2xt/fX6eMWr58eVOHLRjIwIEDOXHiBImJiWYx33Hjxo0MGDCAffv20bFjR1OHIwgmI5I1QRC0ZGVl6ZRRo6KiNGXU6tWr6yRwnp6eZnFmpPDyLly4QP369Vm6dCnvvPOOqcMhKysLPz8/6tWrx44dO0wdjiCYlEjWBEF4JpVKxeXLl3XKqPfv3wfAycmp2DKqKFtZjo4dO5KcnExMTIxZ7GE2depU5s+fT2xsLF5eXqYORxBMSiRrgiC8tLt37+psJ5KYmIgkSdjY2GjKqEXbiYSEhFCuXDlThy38zdGjR2nTpg2bN2+mT58+pg6HK1euEBgYyOTJk5k5c6apwxEEkxPJmiAIepWVlUV0dLTWsVpRUVHk5uYCUKNGDZ0yqoeHhyijmogkSTRu3BhJkjhz5oxZvA/dunUjKiqKuLg4ceSaICCSNUEQjECpVBZbRn3w4AEAzs7OWmXUunXrEhAQgJ2dnYkjL/m2bdtG7969OXz4MG3atDF1OOzZs4euXbuazSifIJgDkawJgmASkiRx9+5drRG4yMhILl++DICtrS0BAQFaI3AhISG4urqaOPKSQ6lUEhQURPXq1Tl48KCpwyE3N5c6derg6enJwYMHzWKUTxDMgelnkQqC8FqSyWS4u7vj7u5O586dNbdnZGQQHR2tlcBt2LBBU0atWbOmThm1Zs2aomN/CStXriQ+Pp7Vq1ebOhQAvv32W65fv86uXbvE+ykIfyFG1gRBMHtKpZLExEStY7UiIyN5+PAhAC4uLjoJnL+/vyijPkVubi61a9emadOmbNiwwdThcPPmTfz8/Bg9ejTffPONqcMRBLMikjVBECySJEmkpKTorEa9cuUKUFhGDQwM1Cmjuri4mDZwM/Htt98yadIkYmNj8fHxMXU4DBgwgOPHj5OQkICTk5OpwxEEsyKSNUEQSpT09HStMmpERAQxMTHk5eUB4OHhodlKpOhf9erVX6uy25MnT/Dy8qJv37785z//MXU4mq1DVqxYwbBhw0wdjiCYHZGsCYJQ4hUUFJCQkKCzGvXx48cAuLq6FltGtbW1NXHkhvHZZ5/xzTffkJSURJUqVUwaS0FBAfXq1cPJyYnff/8dKysrk8YjCOZIJGuCILyWJEni9u3bOmXUpKQkAOzs7DRl1KKRuODgYJydnU0c+au5d+8eXl5ejB07ljlz5pg6HL7//ns+/PBDzp07R7169UwdjiCYJZGsCYIg/MWTJ0+IiorSSuBiYmLIz88HwMvLS2cUrlq1ahZTRh0zZgxr1qzh6tWrJt8G5d69e/j4+DB48GCWLFli0lgEwZyJZE0QBOEZCgoKiI+P1ymjpqamAlCuXDmt5K1u3br4+vqaXRn16tWr+Pr6MmvWLCZPnmzqcBgxYgQ7duwgMTGR8uXLmzocQTBbIlkTBEF4CZIkcevWLZ3tRK5duwZAqVKlqFOnjlYSFxwcbNKVjm+//TZHjhzhypUrJj/G6cyZMzRu3JglS5bw3nvvmTQWQTB3IlkTBEHQo7S0tGLLqAUFBQDUqlVLp4xatWpVg5dRL168SN26dVmyZAnvvvuuQdt6FrVaTaNGjVCpVPz5559YW1ubNB5BMHciWRMEQTCw/Px8rTJq0UhcWloaABUqVNBJ4Hx9fbGx0d8hM126dOHy5ctcunTJ5OXZpUuXMnLkSP744w/efPNNk8YiCJZAJGuCIAgmIEkSN27c0FmNmpycDBSWUYOCgnTKqI6Oji/c1vHjx2nZsiUbNmygf//+en4lLyY1NRUfHx86derEypUrTRqLIFgKkawJgiCYkdTUVJ0y6qVLlzRlVG9vb63tREJDQ3F3d//HMqokSTRr1ozc3Fz+/PNPk+9jNnbsWFasWEFCQgLu7u4mjUUQLIVI1gRBEMxcfn4+sbGxOqNwT548AcDNzU2njOrj44ONjQ07d+6kR48eHDhwgA4dOpj0dURFRVG3bl3mzZvHhAkTTBqLIFgSkawJgiBYIEmSuH79uk4Cd/36dQBKly5NnTp1SEpKws3NjfDwcIKDgylbtqzJ4m3ZsiUPHjzg4sWL2NnZmSQOQbBEIlkTBEEoQVJTU7l48SKRkZFs3ryZEydOYG1tjUqlQiaTUbt2bZ1RuMqVKxt8Neq6desYPHgwBw8epH379gZtSxBKGpGsCYIglEB5eXn4+vpSv3591q5dW2wZNT09HYCKFStqbegbGhpK7dq19balRmZmJr6+vjRu3JgtW7bo5ZqC8DoRyZogCEIJ9P333/PRRx9x6dIl/Pz8dO6XJInk5GSdBO7GjRsA2NvbExwcrDUCFxQURJkyZV44lilTpvD9998THx9PzZo1X/m1CcLrRiRrgiAIJUxGRgZeXl706NGDpUuXvtBzHz16pCmjFv2LjY3VlFF9fHx0jtaqVKnSP14vISGBoKAgpk2bxueff/6qL00QXksiWRMEQShhZsyYwf/93/9x+fJlqlev/srXy83NLbaMmpGRAUDlypV15sF5e3tjZWVFp06dSEhIIDY2Fnt7+1eORRBeRyJZEwRBKEHu379PrVq1ePfdd/nmm28M1o5arSY5OVnrXNTIyEhu3boFgIODA9WrVychIYHRo0czfPhwgoKCTH4mqSBYIpGsCYIglCDjx48nPDycq1evUr58eaO3//DhQy5evMiff/7JrFmzkMlk5OTkoFKpsLKywtfXV2cUrmLFikaPUxAsif4OnhMEQRBMKjk5mSVLlvD555+bJFGDwnNO27Zty8mTJ8nPzyc6OpqaNWty6dIlrXNRd+3aRWZmJgDu7u7/WEYVBEGMrAmCIJQYw4cP58CBAyQlJb3Uqk19uX79On5+fnzwwQfMnTu32Meo1WquXr2qMw/u9u3bAJQpU4bg4GCtY7Xq1Kkj5r0JryWRrAmCIJQAMTExBAcHs3jxYt5//32TxtK3b19OnTpFfHz8Cx88X3TCQVHyFhERQXx8PGq1GisrK/z8/HRG4dzc3Az0SgTBPJSoZO3mzZt88cUX7Nu3j4cPH+Lu7k7Pnj2fuyTQqlUrjh07BoCdnR0VKlSgXr16hIWF0bt3b0OHLwiC8NJ69OhBTEwMcXFxJj3K6dChQ7Rv3541a9YwePBgvVwzJyeHmJgYrRG4ixcvkpWVBUCVKlV0NvX18vISZVShxCgxydrVq1dp0qQJPj4+zJo1C09PTy5dusTHH39Mfn4+p0+fxtXVlWylRIFaQi2BlQxsrWQ42MiQyWS0atUKHx8fZs6cSUFBAbdv32bbtm0sWLAAuVzOzz//bOqXKQiCoOPEiRM0a9ZMrwnSy8jPzyckJAQ3NzeOHTtm0COs1Go1SUlJOmXUlJQUAMqWLUtISIjWCFydOnUoXbq0wWISDEeSpKf23yVdiUnWOnXqRExMDImJiZo5DdlKNeeu3OStegG82XMAnSfPI1+t+1w7K6jsYMO8Yd0IDgnlp8Xf42Dzv29k4eHhjBgxgl9//ZV27doZ6yUJgiA8kyRJtGjRgoyMDC5cuGDS0aT58+czadIkLly4QEhIiEliuHfvns6mvgkJCajVaqytrYsto1aoUMEksQr/LFup5npGAXezldzJLvzfp/Xf7g62VHawoaajrVb/XVKUiGTt8ePHVKhQgdmzZzNlyhRSspVceJBDXGo+amDblx8RfXgXnx1NfGoG/svIHlT2qUOPj2fj72pHPTd7qjjYIEkSFSpUYODAgfz444/Ge2GCIAjPsHfvXrp06cLevXvp1KmTyeK4c+cOvr6+DBs2jMWLF5ssjuJkZ2cXW0bNzs4GoFq1ajoJnKenpyijGpkkSTr9txVQTI6mo+hxVqDVf5eUUbcSsXXH5cuXkSQJ5xreLItP42GuChlQlIW6efqQk55GVupDypb754moRY9XA7Gp+VxKzcettDUtqjjg4+NDcnKyYV+IIAjCC1Cr1XzyySe0aNGCjh07mjSWKVOmUKpUKWbOnGnSOIrj4OBAw4YNadiwoeY2lUpFUlKS1qa+S5cu5e7duwA4OjrqlFEDAwNFGdVAEtPyOH4nW6f/fp5E7a+PK67/ru1cSu/xGluJSNZylYVv06l7OdTJVQH/e6ML//u//+8FMuyi5z/MVbHlagaPc5W4vHqogiAIerNu3TqioqI4efKkSUcQTp48ycqVK/n5558pV66cyeJ4EdbW1vj4+ODj48OAAQM0t9+9e1erjHro0CF++OEHJEnCxsYGf39/rQQuJCTEZHvalQQ5SjUHb2USl5pP0W/wq5b7/t5/+7vm0aFaWewtuDxq8claYloeJ1QVkMlk3L+agNS6s85jHly7jL2TC2VcXvwDJQFqlYpb15Ko5BdKYloePi6Wn6ULgmDZ8vPz+eyzz+jRowdNmjQxWRwqlYoxY8bQoEEDRowYYbI49KVy5cpUrlyZt956S3NbVlaWThl1y5YtmjJq9erViy2jlpQSnKEkpuWx70YmuarC9Erfc7KKrhefmk9yeiqdapS12P7bYpM1SZI4dS+H43eysXJ0xbtRS05vCqfZkPewLf2/TRMzHt4jct8W6nXt/9IfnAu71pOTnoZfm65svZZBS3cVjSvZiw+iIAgm88svv5CcnMyuXbtMHkdERASnT5/G2trapLEYSpkyZWjUqBGNGjXS3KZSqbh8+bJWAvef//yH+/fvA+Dk5KSTwAUEBFCqlGUmC/r01/7bKO0BOSrJovtvi1xgIEkSx+5kc/pejua2hzeS+EneBTfP2nR4/xNcq9bkXlI8+76fgTI/j/dX7MfB2fWp1/15ZA8q1KhF+9GTUatUPLl/h0tH9nBizU806PU2PT+Zp3lsk0r2tHB3sLg3XBAEy5eZmUmtWrXo1KkTCoXCZHE8evQIHx8fevTowfLly00Whzm5e/euznYiiYmJmjJqQECAThnVUkrH+lBc/21slth/W2SydvJudrEZeWrKTQ7/Zx6Jp46QnZZK2QoVCWjVibajPqaMy7M/DD+P7MG18ycBsLa1w8HZlar+ITToMZjANl10Ht/S3YEmlR1e/QUJgiC8gFmzZvHll1+SmJhIzZo1TRbH6NGjWbt2LYmJiVSqVMlkcZi7rKwsoqOjtRYzREVFkZubC0CNGjW0NvQNDQ2lZs2aFpVMPK9/6r+NzdL6b4tL1hLT8th6LcPUYWj09nS02Bq4IAiW5+HDh9SqVYsRI0awYMECk8Vx4cIFGjRowLfffsv48eNNFoelUiqVOmXUiIgIHjx4AICzs3OxZVRTnk7xqkT//fIsKlnLUar5OTaVHJX5hGxvLWNUgKtFrzIRBMFyTJgwgV9++YWkpCSTnYkpSRLNmjXjyZMnREREYGtra5I4ShpJkrTKqEUjcVeuXEGSJGxtbbXKqHXr1iUkJAQXFxdTh/5Mov9+NRaVrO1ITic+Nf+lVoxcu3AKxdiB/3j/jBPXXyomGYUb8HX3cHqp5wuCIDyvGzdu4OPjw6effsrnn39usjhWrVrFsGHDOHLkCK1btzZZHK+LjIwMoqOjtUbhoqOjNWVUDw8PnVG4GjVqmFUZ9VX6b0OxpP7bYpK1Vx0+LcjN4cn9O/94f4UaXi99bYA+Xo4lYuM9QRDM1zvvvMOuXbtISkrC0dHRJDGkp6fj6+tLixYt2LBhg0liEArLqImJiTpl1IcPHwLg4uKik8D5+/ubpIxqbuXPv7OE/tsikjVJklgWn8ajXJVZZeVFZECF0taM8HMxq28ygiCUHHFxcdSpU4cFCxbwwQcfmCyOiRMnsmTJEuLj46levbrJ4hB0SZJESkqKzmrUK1euAGBnZ0dgYKDOalRnZ2eDxiT671dnEcna7awCViU+MXUYzzTUx5mqZcTcDUEQ9K93795EREQQHx9vsr264uLiCA4OZsaMGXz66acmiUF4cRkZGURFRemUUfPy8gDw9PTUGYWrXr36cyUvX375Jba2tnz88cfF7rMn+m/9sIhkbVdyOrFmVuv+OxkQ4GpHNwuofQuCYFnOnDlD48aNWblyJUOHDjVJDJIk0aFDB65du8alS5fE5q4WTqlUkpCQoLWdSGRkJI8ePQKgXLlyOgmcn5+f1mISSZJwdHQkKyuLNm3asG7dOipWrKjVjui/9cPsk7VspZrF0Y+f+zBXU7ICxgSVw8ECVpYIgmAZJEmiTZs2PHz4kMjISJOdErB161b69OnD7t276dJFd99JwfJJksTt27d1yqhJSUlAYRm1Tp06muStYsWKDBxYuHDP2tqa8uXLs3nzZpo3bw6I/lufzD5Zi0vNY0ey+U5M/LueHo74uYpvnIIg6MeBAwfo2LEjO3fupFu3biaJITs7G39/f4KCgti9e7dJYhBMJz09XVNGLRqJi4mJIT8/X+txRWXTTz75hC+//JKEJwWi/9YTs0/Wjt7O4s/7ORaTmTesaE+rqmVMHYogCCWAWq2mQYMGODg48Pvvv5tsAvT06dOZM2cOly5dwtvb2yQxCOaloKCAsWPH8ssvv6BW6/bQ77//Pn0/nSf6bz0x+4Pc72QXWMQbDaAGUrKVpg5DEIQSYtOmTURERJg0Ubt69Spz585l4sSJIlETNGxtbbl16xZqtRobGxuUSiVWVlbUrVuXOnXqMHnyZP4Q/bfemHWyJkkSd834h1ecu9lKJEky6yXAgiCYv4KCAqZNm0aXLl1o1qyZyeL46KOPcHNzE6s/BR1qtRo3Nze6dOlCly5daNeuneY0BUmSuBv1yLQBviBz7r/NOlnLVkrkW0pa/l/5aolspUQZW/N7swVBsBzLli0jKSmJLVu2mCyG/fv3s2PHDjZs2ECZMuZZHhJMZ8+ePQDFJjei/9Yvs07WCtRmPZ3uHynNexqgIAhmLjs7mxkzZjBkyBCCg4NNEkN+fj7jxo2jdevW9OvXzyQxCObtaSNQov/WL7NO1iz0vUZlYd8mBEEwLwsXLuTRo0fMmDHDZDF89913mpE9cywLCeZN9N/6ZZ4bivyXlYX+fbA265+qIAjm7PHjx8yZM4d3330XL69XO7P4Zd2+fZuZM2cyZswY6tSpY5IYBMsm+m/9MtOwCtla6LttI76FCoLwkubOnYtSqWTatGkmi2HSpEmUKVOGL774wmQxCJZN9N/6ZdbJmoONDDuzjlCXVJDPr3t3cf36dcx8CztBEMzM7du3WbhwIR999BGVKlUySQzHjx9n7dq1zJkzR7OyTxBelCX233ZWMhxszDNZM/tNcddeTuNGpmVs3yFJEjcvnmHJiMJdxl1dXXXOVvP399c6W00QBKHIqFGj2Lp1K1evXsXJyfjnFCqVSurXr4+9vT0nT57EysrCelvBrFhS/w1Qo6wtg2s7mzqMYpn1AgMAdwdbbmUqLWJjPWuZjH4dWvHpzZta56rt3LmTBQsWAIVnqwUGBmolcCEhITg7m+cviCAIxpGQkMDy5cv5+uuvTZKoAfznP/8hOjqaM2fOiERNeGWW1H9bAVUczDclMvuRtZJyNuizzlbz9PQkNDSUunXrapK4atWqiVVYgvCa6N+/P6dPnyYxMZHSpUsbvf0HDx7g4+ND3759+eWXX4zevlDylJT+2xyYfbKWrVSzOPqxRWTmapWS2rf/pEent7Czs3vm4wsKCoiPj9cahYuIiCA1NRWAcuXK6ZRR/fz8RBlVEEqYc+fO8cYbb7B8+XLCwsJMEsOoUaPYtGkTiYmJuLm5mSQGoWSxpP7bChgTVA4HG/McUTb7ZA1gV3I6san5mHWgkprrp4/y078HUr58eYYMGYJcLqdu3bovdhlJ4tatWzoJ3LVr14DCMmqdOnU0yVvdunUJDg42WdlEEIRX1759e27fvk1UVBQ2NsYvxZw7d46GDRuycOFCxowZY/T2hZLLEvpvGRDgakc3D/PtRy0iWbudVcCqxCemDuOZhvo4k3otgRUrVrBq1Sru3btHSEgIcrmcwYMHU7FixZe+dlpamqaMWvQvJiaGgoICAGrVqqUzCle1alVRRhUEM3fo0CHat2/Ptm3b6Nmzp9HbV6vVNG3alJycHM6fP2+SZFEouSyp/65axnyrVhaRrEmSxPL4NB7mqswyO5cBFUpbM8LPRZMcKZVK9u/fj0KhYOfOnUiSRJcuXZDL5XTp0kUvpcz8/Pxiy6hpaWkAlC9fvtgyqvhjLAjmQZIkGjZsiLW1NadOnTLJl6vw8HBGjBjBsWPHaNGihdHbF0o2S+y/zZFFJGsAl5/kseWq+U5U7OPlSG3n4icmPnr0iHXr1qFQKDh//jxubm6aMmlISIhe45AkiRs3bmglcJGRkSQnJwNQqlQpgoKCtBK44OBgHB0d9RqHIAjPtnnzZvr168fRo0dp1aqV0dtPS0vD19eXtm3bsnbtWqO3L7weLLn/NhcWk6wB7EhOJ97Mat8ywN/Vju7PWeuOjo5GoVCwevVq7t+/T2hoqKZMashJvWlpaVy8eFErgbt06ZKmjOrt7a0zClelShWz/qYhCJZMqVQSGBiIl5cX+/btM0kM48ePZ+nSpSQkJFC1alWTxCC8HkpC/21KFpWs5SjV/BybSo7KfEK2t5YxKsAV+xdcQVJQUKApk+7atQuArl27IpfL6dSpk1FWfObn5xMXF6fZSqTo35MnhfML3NzcdBI4Hx8fUUYVBD1YunQpI0eO5MKFCy+8EEkfYmJiCA0NZfbs2UyePNno7QuvlxylmqAmzalUuw7dPp6tdd+lo3tZPWE4/3fhAQDKgnxOrv2Ziwe28iA5CSsba1zda+DfogON+4/Aya3yc7d7/eJZ/vNON7wbtWTEDxu17nvZ/tsULCpZA0hMy2PrNfMZTu3t6YiPy6sNnz58+JB169YRHh5OREQEbm5uvP3228jlcoKDg/UU6fORJInr16/rlFGvX78OQOnSpYsto5YtW9aocQqCJcvJyaF27do0b96cdevWGb19SZJo27atZgVqqVLmXQISSoZGzVpgU8P/qcmaMj+P5e/3487lWNq9NwmPkEaUKuvI41vJxP62j9KOTnQc+9lzt7ll5njs7MtwbvtqPtx8Ahf3apr79NF/G4vFDZH4uJSihbuK43eyTR0KLd0d9PJGV6hQgbFjxzJ27FguXrzIihUrWL16NQsWLKBevXrI5XIGDRpEhQoV9BD108lkMjw8PPDw8NBamZaamqopo0ZERHDmzBnCw8NRKpXIZDJNGfWvm/pWrlxZlFEFoRiLFy/m3r17fPnllyZpf9OmTRw9epR9+/aJRE0wGnsbKyqWeXra8cean0iOPMOY1b9Sxe9/gxUVanjh07TNC525nZ+TRfSvO/j3ql/JfHSf87vW03bUREB//bexWNzIGhR+Kzx+J5tT93JMFkOTSva0cHcwWDJSUFDAvn37NGVSmUxGt27dkMvldOzY0Sw2xs3LyyM2NlZnFC49PR2AihUrFltGtba2NnHkgmA6aWlpeHl5MXDgQH788Uejt5+VlYWfnx/169dn+/btRm9feH21atWKkJAQek/+Sqv//uvI2vcDWuFYoaJOyfJlnNu+htObFYxZ/Stxxw+ya94nfLzrHE0rOxi0/zYEixtZg8LRnxbuDthZyThmghG2lu4ONKnsYNA2bG1t6d69O927d+fBgwesXbsWhUJB9+7dqVixoqZMGhQUZNA4nqZUqVLUrVtXa76NJEkkJydrJW/r169n3rx5ANjb2xdbRi1TpoypXoYgGNXXX39Nbm4un332/KUcffrqq6948OAB3377rUnaF15vz+q/H95IwqtBU63bVk0YzpXTvwFQuXYgoxV7n6utczvWULdzXwB8mrYhPzuLMlfO0LJe11d7ESZgkckaFL7hTSo7UL60NftuZJKrkgy6ykQGlLaW0alGWaMPnbq5uTFu3DjGjRtHZGQkK1asYOXKlXz77bfUr19fUyYtX768UeMqjkwmw9PTE09PT3r16qW5/dGjR1qrUU+dOsWyZctQqVTIZDJ8fHx0RuEqV37+SaSCYAnu3LnDggULGD9+PO7u7kZv/8qVK3zzzTdMmTIFLy8vo7cvCKDbf+uOb2nf0mPKXPJzsjm5/heSL5x6rjYeJF/h5qUIhnyjQAaULWVLz779OL19LR/0F8ma0fm4lKJ6WVsO3sokLjUfGeg1aSu6nr+rHe2rlTX5qpGiRGbu3Lns3bsXhULBhx9+yEcffUT37t01ZVJzW7FZvnx52rRpQ5s2bTS35ebm6pRR9+7dS0ZG4QKSSpUq6SRwtWvXFmVUwWJ9+eWXlC5dmkmTJpmk/fHjx1O5cmWx+lMwCScnJ81uA/C//nvywSxKlXUs3KC2uhcPki9rP++/qz8dnFyfu61z29egViqZ07Fw3puMwsqPra0tqampuLo+/7XMgXn16C/J3saKHh5O+Lvk8fudbB7kql45aSt6foXS1rSo4mB2G+bZ2dnRs2dPevbsyf3791m7di3h4eF069aNSpUqMXToUORyOYGBgaYO9R+VLl2aevXqUa9ePc1tarWa5ORkre1E1qxZw9y5cwFwcHAgODhYK4ELCgrCwcGwZWlBeFVXrlzhl19+4auvvsLFxcXo7e/Zs4c9e/awefNm8XkRTMLPz09nT0F7GytU12Lw8fGlQmlrQjr24uCP/0dKfJTWAoMXoVYqubBnA/0nfcmwnp2p6Winua9Pnz6sWbPG4s7AtcgFBk8jSRIp2UouPMghLjUfNWAFqJ/juUWPs5JBgEsp6rmVxt3BxqImIUZGRqJQKFizZg0PHz6kQYMGmjJpuXLlTB3eS3v48KHOpr5xcXGoVCqsrKyKLaNWqlTJ1GELgsagQYP4/fffuXz5Mvb29kZtOzc3lzp16uDp6cnBgwct6m+aUHIkJycTEBBAWFgYo0aNwt7enl9//ZUJEyawatUq+vbty9XHmXTr2IHkhDjavvsxXnUbU8rJhYfXk/j1p7molUrGrj2sc+2/9t9Pzv7KN2Pl3Lt3T+eL0dSpU9m7dy8RERFGec36UuKStb/KVqq5kVHAnWwld7KV3M0uIL+YrM3OCio72FLFwYbKDjbUcLTFwQI2yXua/Px89uzZg0KhYM+ePVhbW9O9e3fCwsLo0KGD2ZVJX0Zubi6XLl3SbCcSGRnJxYsXyczMBKBy5co624l4e3tjZWXZ761geSIiIqhXrx6//PIL//rXv4ze/ldffcX06dOJiorC39/f6O0LQpHz588zdepUIiIiyM3NxcfHhwkTJjBw4EDNY/Ly8pj37QLWrl3HtSuXUavVuFatQe2mbWk25D1cKv/vtI3i+u8BvXqgVqvZs2ePTvsXLlygfv36nD9/XquqY+5KdLL2d5Ikka2UUEoSKjVYW4GNTIaDjaxEf9O8d++epkwaHR1N5cqVNWXSgIAAU4enV2q1mqtXr+psJ3L79m0AypQpo1NGrVOnjigLCQbVqVMnrl69yqVLl4z+RenmzZv4+fkxevRovvnmG6O2LQj68rr230Veq2TtdSdJEhEREZoy6ePHj2nYsCFyuZyBAwda3ITLF/HgwQOtMmpERATx8fGo1WqsrKzw9fXVJG9169YlJCSEihUrmjpsoQT47bffaN26NZs2baJv375Gb3/AgAEcP36chIQEnJzM/wxEQRB0iWTtNZWXl8eePXsIDw9n3759WFtb07NnT+RyOe3bty8RZdJnycnJISYmRmsE7uLFi2RlZQFQpUoVnXlwtWrVEmVU4blJkkSTJk1QqVScPXvW6CMAR48epU2bNqxcuZKhQ4catW1BMJQbN248tSoUGxtLjRo1jBiR4YlkTeDu3busWbOG8PBwLl26RJUqVRg6dCjDhw9/7ea3qNVqkpKSdMqoKSkpQGEZNSQkRKeMauwJ44Jl2L59O7169eLQoUO0bdvWqG0XFBRQt25dnJ2d+eOPP16LUpHwelAqlSQnJ//j/R4eHiVuwEEka4KGJElcuHABhULB2rVrefz4MY0aNUIulzNgwIASXSZ9lnv37umsRk1ISECtVmNtbY2fn5/OKJwxznIVzJdKpSIoKIiqVavy66+/Gr3977//ng8//JBz585Z1ERqQRB0iWRNKFZeXh67du1CoVCwf/9+bGxs6NmzJ2FhYbRr105sTAtkZ2cXW0bNzi48QqVq1ao6CZyXl5coo74mwsPDGTFiBH/++ScNGjQwatv37t3Dx8eHwYMHs2TJEqO2LQiC/olkTXimO3fuaMqksbGxVKlShWHDhjF8+HD8/PxMHZ5ZUalUJCUlaW3qGxkZyd27dwFwdHTUKaMGBgZSunRpE0cu6FPRlgSNGzdm48ZXP5D6RY0YMYIdO3aQmJhoFsfQCYLwakSyJjw3SZI4f/484eHhrFu3jtTUVBo3bqwpk5piV3ZLcffu3WLLqJIkYW1tjb+/v84onOhkLdeCBQv4+OOPuXTpEr6+vkZt+8yZMzRu3JglS5bw3nvvGbVtQRAMQyRrwkvJzc3VKpPa2dnRq1cv5HI5bdu2FWXS55CVlaVVRo2IiCAqKoqcnBwAqlWrprWdSGhoKB4eHqKMauaePHlCrVq16N27Nz///LNR21ar1TRq1AiVSsWff/4pPoeCUEKIZE14ZSkpKZoyaVxcHFWrVtWUSY09qmDpVCoVly9f1hqBi4iI4P79+0DhQcjFlVFLlTKvs2tfZ59//jlff/01V65coWrVqs9+gh4tXbqUkSNH8scff/Dmm28atW1BEAxHJGuC3kiSxJ9//olCoWDdunWkpaXRpEkTwsLC6N+/P87OzqYO0WLdvXtXZzuRxMREJEnCxsam2DKqJZ8Fa6nu3btHrVq1+Pe//83cuXON2nZqaio+Pj506tSJlStXGrVtQRAMSyRrgkHk5uayc+dOFAoFBw4cwM7Ojt69eyOXy2nTpo0oz+hBZmYm0dHRWglcVFQUubm5ANSoUUMngfPw8BD7bRnQ2LFjWb16NUlJSUZPlseOHcuKFStISEjA3d3dqG0LgmBYIlkTDO727dusXr0ahUJBfHw81apVY9iwYcjlcmrXrm3q8EoUpVJZbBn1wYMHADg7O+uUUQMCAkQZVQ+uXr2Kn58fM2fOZMqUKUZt++LFi9SrV4958+YxYcIEo7YtCILhiWRNMBpJkjh79qymTPrkyRPefPNN5HI5/fv3F+cWGogkSdy9e1dnO5HLly8DYGtrS0BAgFYCFxIS8lpvgvwyhg4dyuHDh7ly5QoODg5Ga1eSJFq2bKk5/9bOzs5obQuCYBwiWRNMIicnhx07dqBQKDh48CClS5emT58+yOVyWrduLVY8GkFGRoZOGTU6OlpTRq1Zs6ZOGbVmzZqijFqMqKgoQkND+fHHH42+XcbatWsZMmQIBw8epH379kZtWxAE4xDJmmByt27d0pRJExISqF69OsOHD2f48OF4e3ubOrzXilKpJDExUaeM+vDhQwBcXFwICQnRbCUSGhqKv7//az+a07VrVxISEoiNjcXW1tZo7WZmZuLr60vjxo3ZsmWL0doVBMG4RLImmA1Jkjhz5gzh4eGsX7+e9PR0mjVrpimTOjo6mjrE15IkSaSkpOisRr1y5QpQWEYNDAzUKaO+Lpsk//7777Ro0YL169czYMAAo7Y9ZcoUvv/+e+Lj46lZs6ZR2xYEwXhEsiaYpZycHLZv345CoeDXX3/F3t5eUyZt1aqVKJOagYyMDKKionTKqHl5eQB4eHjobOpbvXr1ElVGlSSJ5s2bk52dzblz54z6e5mQkEBQUBDTpk3j888/N1q7giAYn0jWBLN369YtVq1aRXh4OJcvX6ZGjRqaMmmtWrVMHZ7wF0qlkoSEBJ3FDI8ePQLA1dVVZx6cv7+/UUuH+rRr1y66d+/O/v37eeutt4zWriRJdOrUicTERC5duoS9vb3R2hYEwfhEsiZYDEmSOHXqFAqFgg0bNpCenk6LFi2Qy+X07dtXlEnNlCRJ3L59W6eMmpSUBICdnV2xZVRz30RZpVIRGhqKm5sbhw8fNuqI4Y4dO+jZsyfbt2+nR48eRmtXEATTEMmaYJGys7M1ZdJDhw5hb29P3759kcvltGzZUpRJLUB6erqmjFo0EhcTE0N+fj4AXl5eOqNw1apVM5sy6qpVqxg2bBinT5+mUaNGRms3JyeHwMBAfH192bt3r9n8PARBMByRrAkW78aNG6xatQqFQsGVK1fw8PBg+PDhDBs2DC8vL1OHJ7yAgoIC4uPjdVajpqamAlCuXDmdBM7Pz8/oZdS8vDz8/PyoW7cuW7duNWrbM2fOZNasWcTExODj42PUtgVBMA2RrAklhiRJnDx5UlMmzcjIoGXLlpoyadmyZU0dovASJEni1q1bOgnctWvXgMIyap06dbS2EwkODjboJssLFy7kww8/JCYmBn9/f4O183fXr1/Hz8+PDz74wOhnjwqCYDoiWRNKpOzsbLZu3YpCoeDIkSM4ODjQt29fwsLCaN68uSiTlgBpaWk6q1FjYmIoKCgAoFatWjqjcFWrVn3lsmFGRga1atWiW7duLFu2TB8v5bn17duXU6dOER8fL+ZoCsJrRCRrQol3/fp1TZk0KSkJT09PTZnU09PT1OEJepSfn09cXBwXL17UGoVLS0sDoHz58jrbifj6+mJjY/PcbcycOZOvvvqKxMREatSoYaBXouvQoUO0b9+eNWvWMHjwYKO1KwiC6YlkTXhtSJLEiRMnNGXSzMxMWrVqpSmTlilTxtQhCgYgSRI3btzQWY2anJwMQKlSpQgKCtIagQsODi525OrBgwd4eXkxatQo5s+fb7TXkJ+fT0hICG5ubhw7dkwsKhCE14xI1oTXUlZWllaZtGzZsvTr1w+5XE7z5s1FZ/gaSE1N1SmjXrp0SVNG9fb21krg6taty7x58wgPD+fq1auUL1/eaLHOnz+fSZMmceHCBUJCQozWriAI5kEka8JrLzk5WVMmvXr1Kl5eXppNd8URPq+X/Px8YmNjdUbhnjx5onlMrVq16N27tyaJ8/HxeaEy6ou6c+cOvr6+DB8+nEWLFhmsHUEQzJdI1gThvyRJ4vfff0ehULBx40aysrJo06YNcrmc3r17izLpa0qSJK5fv86IESM4e/YsrVq1IiYmhuvXrwNQunTpYsuo+lp9PGzYMPbt20diYiKurq56uaYgCJZFJGuCUIzMzExNmfTo0aOULVuW/v37I5fLadasmSiTvmZiYmIIDg5m0aJF/Pvf/wbg8ePHWgsZIiMjiY2NRalUIpPJqF27ts5q1MqVK7/Q786JEydo1qwZP//8MyNHjjTUyxMEwcyJZE0QniE5OZkVK1awYsUKrl27Rq1atZDL5QwbNsyoqwEF0+nZsydRUVHEx8djZ2f3j4/Ly8srtoyanp4OQMWKFXUSOB8fH6ytrXWupVKpaNCgATY2Npw+fbrYxwiC8HoQyZogPCe1Wq0pk27atIns7GytMqmDg4OpQxQM4NSpUzRt2pTVq1czZMiQF36+JEkkJydrHasVGRnJzZs3AbC3tyc4OFgrgQsKCmLVqlWMHj3a6MdZCYJgfkSyJggvITMzky1bthAeHs6xY8dwdHRkwIAByOVymjZtKsqkJYQkSbRq1Yq0tDQiIiL0upnyo0ePii2jqlQqZDIZMpmMmjVrMmrUKK0yqiAIrx+RrAnCK7p69SorV65kxYoVJCcn4+3tjVwuZ+jQoaJMauH27dtH586d2bNnD507dzZ4e7m5ucTGxjJhwgROnjxJvXr1uHTpEhkZGQBUqlRJ61it0NBQvL29RYlUEEo4kawJgp6o1WqOHz9OeHg4mzdvJicnh7Zt2xIWFkbPnj1FmdTCqNVq6tWrh5OTk1E3or1w4QINGjRgwYIFjBs3DrVazbVr13Tmwd26dQsABweHYsuo4vdNEEoOkawJggFkZGSwefNmFAoFx48fx8nJSVMmbdKkiSiTWoC1a9cyZMgQTpw4QdOmTY3SpiRJNGvWjCdPnhAREYGtre0/Pvbhw4c6ZdS4uDhUKhVWVlb4+PhoHasVGhpKxYoVjfI6BEHQL5GsCYKBJSUlacqk169fp3bt2prVpNWqVTN1eEIx8vPz8ff3p06dOuzYscNo7a5atYphw4Zx5MgRWrdu/cLPz83NJSYmRiuBu3jxIpmZmQC4u7vrrEb19vbW61w8QRD0TyRrgmAkarWa3377DYVCwebNm8nNzaV9+/bI5XJ69uyJvb29qUMU/uvHH39kzJgxREVFUadOHaO0mZ6ejo+PDy1btmTDhg16u65arebq1as6ZdTbt28DUKZMmWLLqOL3URDMh0jWBMEE0tPTNWXS33//HScnJwYOHIhcLqdx48aiTGpCmZmZeHt789Zbb7FixQqjtTtx4kSWLFlCfHw81atXN3h7Dx484OLFi1rbicTHx6NWq7GyssLPz09nFM7Nzc3gcQmCoEska4JgYleuXNFsunvz5k18fX01q0mrVq1q6vBeO7Nnz2bmzJkkJCTg4eFhlDbj4uIIDg5mxowZfPrpp0Zpszg5OTnFllGzsrIAqFKlik4CV6tWLVFGFQQDE8maIJgJtVrN0aNHUSgUbNmyhby8PE2ZtEePHqIsZQSPHj3Cy8uLsLAwvvvuO6O0KUkSHTp04Nq1a1y6dIlSpUoZpd3npVarSUpK0hqBi4yM5M6dOwCULVuWkJAQrQQuMDBQ/L4Kgh6JZE0QzFB6ejqbNm1CoVDwxx9/4OzszKBBg5DL5TRs2FCUSQ3k448/5qeffiIpKcloKye3bt1Knz592L17N126dDFKm/pw7949ndWoCQkJqNVqrK2tiy2jVqhQwdRhC4JFEsmaIJi5y5cva8qkt27dws/PT1MmrVKliqnDKzFu3rxJ7dq1mTJlCl988YVR2szOzsbf35+goCB2795tlDYNKTs7W1NGLRqJi4qKIjs7G4CqVavqbCfi6ekpyqiC8AwiWRMEC6FSqbTKpPn5+XTo0IGwsDC6d+9O6dKlTR2iRfvXv/7Fjh07uHr1Ko6OjkZpc/r06cyZM4dLly7h7e1tlDaNTaVSceXKFZ3VqHfv3gXA0dGx2DKq+H0WhP8RyZogWKAnT56wceNGFAoFJ0+exMXFRVMmfeONN0SZ9AXFx8cTGBjIt99+y7hx44zS5tWrVwkICGDChAnMnj3bKG2ak7t372qVUSMiIkhMTESSJKytrfH399ckb3Xr1iUkJITy5cubOmxBMAmRrAmChUtMTGTFihWsXLmSW7du4e/vrymTuru7mzo8i9CnTx/Onz9PQkKC0Sb49+zZk/PnzxMfH0+ZMmWM0qa5y8rKIjo6WmsELioqipycHACqV6+uMw/O09NTfDkRSj7pFQwfPlwCJECysbGRPD09pQkTJkiZmZnStWvXNPf99d+QIUM0z8/OzpZcXFwkV1dXKTs7W+vaR48elQApNTVV6/aWLVtK48aNe5WwDeKf4tWHop9lRESE3q8tlBxKpVI6cOCANGjQIKl06dKSlZWV1LlzZ2njxo1STk6OqcMzW2fOnJEAacWKFUZrc9++fRIgbdiwwWhtWiqlUinFxcVJ69atkyZPniy99dZbUsWKFTV9ipOTk9S8eXNp7Nix0vLly6ULFy5Iubm5pg5bEPTK5lWTvY4dOxIeHk5BQQG///47//rXv8jKymLy5MkAHDp0iMDAQM3j/7qce8uWLdSpUwdJkti6dStDhgx51XCeSpIkspUSBWoJtQRWMrC1kuFgIxPfzASLZ21tTYcOHejQoQNpaWmaMmn//v1xdXVl0KBBhIWFUb9+ffH7/l+SJDFlyhQCAwMN/venSH5+PuPGjaN169b069fPKG1asqKVpX5+fgwcOFBz+507d7RG4A4cOMDixYuRJAkbGxsCAgK0RuBCQkIoV66cCV+J8Cpe9/77lcqgcrmctLQ0tm/frrlt5MiR7N69m1OnTuHp6UlERAShoaHFPr9169YMHDgQSZLYuHEjR44cASA5ORlPT0+txw4fPhxAZ0fxa9eukZycTOvWrTl06BCTJ08mNjaW0NBQfvxlGaWqeHE3W8md7ALuZivJV+vGYWcFMdtXsD/8Bx6k3MLT05PPpk1j6NChWvH89bWkpaXh6urK0aNH8fDwKDZehUJBq1atNMfVrF69Gmtra0aPHs2XX36p+QWTyWRs27aNnj17ap7v4uLCd999h1wu1/lFbNmyJb/99luxP1NB+LuEhAQUCgUrV64kJSWFwMBA5HI5b7/9NpUrVzZ1eCb166+/0qFDB3bs2EH37t2N0ua8efP49NNPiYyMNNpRVq+LzMzMYsuoubm5ANSoUUMrgatbty41a9Z8LTp7S5OtVHM9o+C5+u/KDja4O9hS2cGGmo62ONiUvNXFrzyy9nf29vYUFBQ883FJSUmcOnWKrVu3IkkS48eP5+rVq3h5eVG9enW2bNlCnz59SEhIwMnJSTMil5iYSJ06dZg5cyYAbm5uJCcnAzB16lS++eYbpLKujHn/fXoOGc574XuxAop5jzUiDu1h3axP6DZxFl6NWpLw+0HkYWGUKl+Zfp3aPfO1PC1eKEww33nnHc6cOcO5c+cYNWoUNWvWZOTIkc+8NsDZs2dp2LChZpTSzs7uuZ4nCAC+vr783//9H7NmzeLQoUMoFAqmTZvGlClT6NixI3K5nG7dupndZqyGplarmTJlCk2bNqVbt25GafP27dvMnDmTMWPGiETNAMqWLUuTJk1o0qSJ5jalUsnly5e1NvX98ccfefjwIQDOzs468+ACAgLE31kTkCSJlGwlFx7kEJeajxqe2X/nq+FGppJbmUrN4/1d7ajnZk8VB5sSk4jrNVk7e/Ysa9eupW3btprbmjZtqrWHzu+//07dunVZvnw5nTp1wtXVFSgspy5fvpxZs2ZhbW2tGa6uWLEiLi4umufb2dnh4OBQ7IjA6E+mc6VSCA9zVTQcOgbFB4MpyMvFttTTl4D/vuoH6nUbSKP+IwBwqzmaG9Hn+Pz/5pHh2QCvgrynPv9p8UJhMrdgwQJkMhm+vr5ER0ezYMGC507Wis7jK1++/Gs/EiK8PGtra9566y3eeust0tLS2LBhA+Hh4fTr149y5coxePBg5HI59erVKzF/4J5m8+bNXLhwgePHjxvt9U6aNIkyZcoYbR83AWxsbPD398ff35/BgwcDhUnB38uoe/fu5fvvvwfA1ta22DJqUX8l6F9iWh7H72TzMFeFjMIJifD0RO2v1H/539jUfC6l5uNW2poWVRyo7Wz5X0RfOVnbvXs3ZcuWRalUUlBQQI8ePVi0aJFmE8QNGzbg7++veXz16tVRqVSsWLFC88EAePvtt/nwww+ZMWMG1tbWLxRDnqrwbbrs6IljrgoAxwqVAMh6/BAX92pPff79a5d5o/cwrdtqhjTkxLpfeJirIjGl8Fy8XOXz/tpo+/vB3E2aNGH+/PmoVKoXfq2CoA8uLi68++67vPvuu8TFxWlWky5evJg6deogl8sZMmRIif1yUFBQwLRp0+jcuTPNmzc3SpvHjx9n7dq1LF++XOcLnWBcMpmMKlWqUKVKFTp37qy5PSMjQ6uMGhERwfr168nLK/zC7uHhoTMKV6NGjdfiy42h5CjVHLyVSVxqPkU/xVfdoqLo+Q9zVWy5moG/ax4dqpXF3oLLo6+crLVu3ZolS5Zga2tLlSpVsLW1BdCUJqtXr66z2ePevXu5ffs2AwYM0LpdpVJx8OBBOnXq9NztJ6blsSs5AwBrG9v/vcn//fBI0vMlWH//sEmShEwmQwJkssI3eEvSE8p55+HjUuq5Sr3PSyaT8fepg/q8viA8jb+/P3PmzNEqk06dOpXJkyfTuXNn5HI5Xbt2LVFloeXLl3PlyhU2bdpklPaUSiVjx46lUaNGmvm3gvlxdHSkadOmNG3aVHObUqkkISFBaxRu0aJFPHr0CCj84vP3BM7f379EfV4MJTEtj303MslVFfZ/+t5HrOh68an5JKen0qlGWXxcLHOU7ZWTtTJlyrzwztvLli1j4MCBTJ06Vev2OXPmsGzZMjp16qT5RVepVFqPsbOzQ6VSIUkSp+7lcPxOdrGTDl9ERc/aJEecoV7X/yWPN6L+xM2jNgBlXAs3Ynxw/y5br2XQ0l1FRlSETlzFxQtw+vRpnf9fu3Ztzaiam5ub5lBkKDxeqGhk8lnXFgR9sbGxoWPHjnTs2JHU1FTWr1+PQqGgT58+lC9fXlMmrVu3rkWPJGRnZzNjxgwGDx5MSEiIUdr86aefiI6O5uzZs+JoJQtjY2NDYGCg1ophSZJISUnRGoHbtWsX3333HVBYRg0MDNQ6ViskJARnZ2cTvhLz8df+2yjtATkqSdN/N65kb3F/w/S+wOBZHjx4wK5du9i5c6fOBNvhw4fTpUsXHjx4oFmhs3v3bjp37oy9vT1ly5bFw8ODM2fOsOFsHJeyrLF3fvU5BM2HjWHd5H9R1T+YWg2bE3f8IJeO7OGdJVsAsC1tT/WgBhwLX4hrlRoozj/i9x+/1LrGP8ULhWcOfvTRR7z77rtcuHCBRYsWMX/+fM1z27Rpw+LFi2ncuDFqtZrJkydrRiihcB6cvb09+/fvp1q1apQuXVp86AWDcnV1ZfTo0YwePZrY2FhNmXTRokUEBQURFhbGkCFDjHbYuT4tWrSIBw8eaBYpGdqDBw/47LPPeOedd2jQoIFR2hQMSyaTUbVqVapWrUqXLl00t6enpxMVFaU1CrdmzRry8/MB8PT01BmFq169usUlDq9CkiSO3cnm9L0ck7R/7E42+WqJFu4OFvVzN/pXvJUrV1KmTBmtRQhFWrdujaOjI6tWraJq1arMmDGDKVOmUKlSJcaMGQPAxIkTyVbLGNaqAbPa+vHk7q1XjimwdWe6fjyb4yt/4Lu+zTm7ZQV9pi/Eq8Gbmsf0nf49KqWSH95uz65vptJk5GSta/xTvADDhg0jJyeHhg0b8u9//5uxY8cyatQozf3z58+nevXqtGjRgsGDBzNx4kQcHBw099vY2LBw4UL+85//UKVKFXr06PHKr1kQnldAQABz587l5s2b7N27Fz8/P6ZMmULVqlXp0aMH27Zt03RG5i41NZU5c+bw7rvv4uXlZZQ2P/30UwC++uoro7QnmI6TkxPNmjVjzJgxLF26lHPnzmm2E1m1ahW9e/cmPT2d77//nh49elCzZk3Kly9PmzZt+Oijj1i5ciXR0dElehrMqXs5JkvUzCmGF2Vxx00lpuWx9VqGqcPQ6O3p+NQaeKtWrQgNDdUMjwtCSfD48WNNmfTPP/+kfPnyDBkyhLCwsH/cV9EcTJkyhcWLF5OUlESlSpUM3t65c+do2LAhCxcu1PoCJ7zeJEni1q1bOofbX716FSic+lKnTh2tEbjg4GCLr6hYWv9tTiwqWctRqvk5NpUclfmEbG8tY1SA6z+uMhHJmlDSXbp0iRUrVrBq1Sru3r1LSEgIcrmcwYMHm1WZ9Pbt23h7ezNx4kS+/PLLZz/hFanVapo2bUpOTg7nz5/Hxsbos04EC/PkyROdMmpMTIxm5NrLy0unjFqtWjWLKOdZYv9tTiwqWduRnE58av4LrxhZ0LcZaXduFntfz6nzqdu570vHJKNwA77uHk7F3i+SNeF1oVQqOXDgAAqFgp07d6JWq+nSpQtyuZwuXbpozcM0hffee4/NmzeTlJRklBGK8PBwRowYwbFjx2jRooXB2xNKpoKCAuLi4nRG4VJTU4HC/Tf/nsD5+vqa/PP2dy/bfxvSs/pvc2IxydqrDJ+mptxEpSx+DoBj+YqUKlP2VUIDoI+XY4nYeE8Q9OHRo0eaMum5c+dwc3NjyJAhyOVyo63A/KvExEQCAgKYN28eH330kcHbS0tLw9fXl3bt2rFmzRqDtye8XiRJ4ubNmzoJ3LVr1wAoVaqUThk1JCQER0dHk8RrbuXPv7OE/tsikjVJklgWn8ajXJVZZeVFZECF0taM8HOxiOFoQTCm6OhoTZn0/v37hIaGasqkRadzGNqAAQM4efIkly9fpnTpp59oog/jx49n6dKlJCQkULVqVYO3JwhQ+CUhKipK62itS5cuaRYseHt764zCValSxaD9lui/9cMikrXbWQWsSnxi6jCeaaiPM1XLmNfQsyCYi4KCAq0yKUDXrl2Ry+V06tTJYGWb8+fP06BBA5YtW8aIESMM0sZfxcTEEBoayldffcWkSZMM3p4gPE1+fn6xZdS0tDQAKlSoUGwZ9XnnWK5cuRJ7e3v69etX7P2i/9YPi0jWdiWnE2tmte6/kwEBrnZ0s4DatyCY2qNHj1i7di0KhYILFy7g5ubG22+/jVwuJzg4WK9tdejQgZs3bxIdHW3wSf6SJNGmTRtSUlKIjo4Wu9gLZkmSJG7cuKG1qW9kZCTXr18HoHTp0gQFBemsRi3aO/Sv1ylXrhxpaWmMHDmShQsX6oxci/5bP8w+WctWqlkc/fi5D3M1JStgTFA5HCxgZYkgmIuoqChWrFjB6tWruX//PvXq1UMulzNo0CAqVKjwStc+fPgw7dq1Y+vWrfTq1UtPEf+zjRs3MmDAAPbt20fHjh0N3p4g6FNqaioXL17UGoG7dOkSSqUSmUymU0atVKmSZqNnKysrAgIC2LZtm+ZUI9F/64/ZJ2txqXnsSDbfiYl/19PDET9X856oKAjmqKCggP379xMeHs6uXbuQyWR069YNuVxOx44dX7hMKkkSjRo1QiaTcfr0aYPPR8nKysLPz4/69euzfft2g7YlCMaSl5dXbBn1yRPd0qaVlRV2dnasXLmSfv36if5bj8w+WTt6O4s/7+dYTGbesKI9raqWMXUogmDRHjx4wLp161AoFERERFCxYkVNmTQoKOi5rrFlyxb69u3LkSNHaN26tYEjhqlTpzJ//nxiY2ONdjqCIJiCJEkkJyczbdo01q1bR3FpxKeffkq79z8V/beemOd431/cyS6wiDcaQA2kZCtNHYYgWDw3Nzc++OADLly4QGRkJEOGDGHVqlUEBwfToEEDFi9ezKNHj/7x+UqlkqlTp/LWW28ZJVG7cuUK33zzDZMnTxaJmlDiyWQyPD09yc3NRZIkZDIZ1tbWALi4uNC4cWN69Ogh+m89MutkTZIk7prxD684d7OVxX7LEATh5YSEhPDtt99y69Yttm/fTvXq1fnwww9xd3enb9++7NmzB6VS++/EihUrSEhIMNp5nOPHj6dy5cpMnjz52Q8WhBLi7t27WFtb8+abbzJ79mwuXrzI48ePOXXqFG+88Ybov/XIrMugWQVqFsU8NnUYL2xsnXKUsTXrPFgQLNqDBw9Yu3Yt4eHhXLx4kUqVKjF06FDkcjleXl74+Pjw5ptvsn79eoPHsnv3brp168aWLVvo3bu3wdsTBHORnp6OJEnFnggi+m/9MutkLS1PxU+xqaYO44WNDnTF2c7a1GEIwmshMjIShULBmjVrePjwIdWqVSMlJYUzZ85oVqoZSm5uLnXq1MHT05ODBw+a9aaagmBMov/WL/NLH/9CbbZp5NOpLKVILwglQNHZu7dv32b16tXcu3cPgDfffJP+/fuzd+9enTKpvnz77bdcv36dhQsXikRNEP5C9N/6ZdbJmpWF/u2zNuufqiCUTHZ2dsTFxWFjY0NERARz5swhPj6eLl26UL16dSZNmkRsbKze2rt58yazZ89m3Lhx+Pv76+26glASiP5bv8w0rEK2Fvpu24hv2IJgdHfu3GHBggWMGzeO4OBgPvzwQy5evMiFCxfo378/y5cvJzAwkEaNGrFkyRJSU1+tRDNx4kScnJz4/PPP9fQKBKHkEP23fpl1suZgI8POrCPUZWclw8HGPN9sQSjJZs2aRalSpbRWZMpkMurWrcv3339PSkoKW7ZsoVKlSowdO5bKlStrTht40TLp0aNH2bhxI/PmzcPJyXyPqBEEUxH9t36Z9QIDgLWX07iRaTnLf2uUtWVwbd2VMYIgGE5SUhJ+fn7Mnj37uQ5Pv3fvHmvWrCE8PJyYmBiqVKnC0KFDGT58+DNLmgUFBdStWxdnZ2f++OMPMVdNEP6B6L/1x+zzXncHW/MP8r+sgCoOhj0oWhAEXZ9//jkVK1ZkzJgxz/X4SpUq8dFHHxEVFcX58+fp3bs3v/zyCwEBATRu3JiffvrpH8ukP/74I7GxsSxevFgkaoLwFKL/1h+z/zlWdrCxqB2QK5vxmy0IJVFkZCRr167liy++wMHB4YWeK5PJqFevHosWLSIlJYXNmzfj5ubGmDFjcHd3Z+DAgRw4cACVSgUUjsh9/vnnvPvuu9StW9cQL0cQSgzRf+uP2ZdBs5VqFkc/tog3XFKraG/zgAYhdUwdiiC8Njp37kxSUhKXLl3CxkY/f2zv3r2rKZNeunSJKlWqMGzYMBISEjh27BiJiYmUL19eL20JQkllSf23FTAmqBwONuY5hmWeUf2Fg40V/q52mHuxQVKriTuymzdCg6hXrx4LFy7k4cOHpg5LEEq0Y8eOsW/fPmbNmqW3RA2gcuXKTJgwgejoaP7880969erFDz/8wLZt23B2dmbz5s2kpaXprT1BKIkspf+WAf6udmabqIEFjKwB3M4qYFXiE1OH8UyDvMpw8divKBQKdu3ahUwmo1u3bsjlcjp27Iitra2pQxSEEkOSJJo2bUpBQQFnz57Fyspwf2hVKhVvvPEGqampBAQEsH//fuzs7OjVqxdyuZy2bdtqDrIWBOF/LKX/HurjTNUy5ttHm28a+RdVHGxwK21tttm5DHArbU0Np9J0796drVu3kpKSwtdff83Vq1fp3r071apV03xTFwTh1e3cuZPTp08zZ84cgyZqAMuXLyciIoLVq1ezZ88ebt26xcyZM7l48SJvvfUWNWvW5NNPPyUhIcGgcQiCpbGU/tucFxeAhYysAVx+kseWqxmmDuMf9fFypLZzqWLvi4yMZMWKFaxevZqHDx9Sv3595HI5gwYNEvNeBOElqFQqgoODcXd359ChQwZtKzU1FR8fHzp16sTKlSu17pMkiXPnzqFQKFi7di1paWk0adKEsLAw+vfvX+wB14LwurHk/ttcWEyyBrAjOZ341HzMKeCiWnd3j2dvjJmfn8/evXtRKBTs2bMHmUxG9+7dNWVSfc65EYSSTKFQEBYWxtmzZ3njjTcM2tbYsWNZsWIFCQkJuLu7/+PjcnNz2blzJwqFggMHDmBnZ0fv3r2Ry+W0adNGlEmF15ql99+mZlHJWo5Szc+xqeSozCdke2sZowJcsX/BiYn3799n7dq1hIeHExUVRaVKlTSbctapI1aTCsI/yc3NxdfXl4YNG7Jp0yaDtnXx4kXq1avHvHnzmDBhwnM/LyUlhdWrVxMeHk58fDzVqlVj2LBhyOVyateubcCIBcE8Pa3/3jR9DBd2bQDAysYGBydXKtcOIOStXtTrPui5pzmkxEdxdPl3JF84TW5mOs6Vq+JVvynNh43BrWYtnce/bP9tChaVrAEkpuWx9Zr5DKf29nTEx+XVhk8jIyNRKBSsWbOGhw8f0qBBA02ZtFy5cnqKVBBKhu+++46JEydy6dIlfH19DdaOJEm0bNmShw8fcvHixZdaICRJEn/++ScKhYJ169aRlpbGm2++iVwup3///uKoKuG18k/996bpY8h89IC+XyxErVaT+eg+iSeP8Fv493jUbcywBauxfkblKe74QdZ8HIZPk9Y0HTSSctU8yHz8kJhDO0m7e5vBc5fqPEcf/bexWFyyBnDybjbH72SbOgxaujvQpPKLbcL5NPn5+ezZs0dTJrW2tqZ79+6EhYXRoUMHUSYVXnvp6enUqlWLnj178ssvvxi0rbVr1zJkyBB+/fVX2rVr98rXy83NZceOHSgUCg4ePEipUqW0yqSGXiQhCOaguP570/Qx5GakM/Rb7TmhSWd/Z+l7ven92be80WvoP14zPyebeV3qUbNuI4bOX6Fzf07GE+wdteeP6rv/NjSL/OvQpJI9TSrZmzyGxnqOoWgrgB07dnD79m3mzJlDQkICXbp0oXr16kyaNInY2Fi9tikIlmT+/PlkZmYyffp0g7aTkZHBxx9/TJ8+ffSSqAGULl1ac3D8jRs3mD59OufOnaN9+/Z4eHjw2WefceXKFb20JQjm6kX671oNm+PuE0jMkT1PfdzlU0fJSntEi2HFHzf390TNEP23oVlksiaTyWjh7kBLd9NkxS3dHWhZpYxBzwWsVKkSH374IRcvXuT8+fP069ePZcuWERgYSMOGDfnxxx95/PixwdoXBHNz//595s+fz9ixY6lWrZpB25o1axapqanMnz/fINevWrUqkydPJi4ujlOnTtG5c2cWLVpE7dq1ad68OcuWLSMjw3ymewiCvrxo/+3mUZu0lJtPfczDG1cBqOj57Pmgxui/DcEikzUofMObVHagt6cj9tYyg+/hIqNwMmJvT0ejDp0WnV24cOFCUlJS2LJlC5UrV+aDDz7A3d1d801dqVQaLSZBMIXZs2djY2PDlClTDNpOQkICCxYsYMqUKdSsWdOgbclkMs3B8Xfu3GHdunU4ODgwcuRIKleuzLBhwzhy5AhqtSUc2CMIz+fv/ffTSJIEz0ysnj6by1T9tz5ZbLJWxMelFKMCXPFztQPQe9JWdD1/VztGBbiadDJi0RyXnTt3cvv2bb766isuXbpE586dqVGjhuabuiCUNNeuXWPJkiVMmjTJoItuJEli3LhxVKtWjY8//thg7RTH3t5ec3D8jRs3mDZtGmfOnKFt27Z4enry+eefk5SUZNSYBMGQivpvZ7vCbW2K678fXLuMa5UaT71OhRqFKz3vX7usdbs59d+vyuKTNQB7Gyt6eDjR29ORCqX/+U1/EUXPr1Damj5ejnT3cDKr5b2VKlXSnIhw7tw5+vTpw9KlSwkICNB8U09NTTV1mIKgF9OnT6d8+fKMGzfOoO3s3LmTAwcOsGDBAuztTTenpVq1anzyySfEx8dz8uRJOnbsyPfff4+3tzctW7YkPDxclEmFEsHexooaZW1xd7DR6b+Tzv7O3Sux1Gnb9anXqN2kFWVcynN85WKt5xf13y1c1GbVf78Mi1wN+jSSJJGSreTCgxziUvNRU5iRPk8RoehxVjIIcClFPbfSuDvYWExtOy8vj127dqFQKNi/fz82Njb07NkTuVxO+/btxaacgkWKjo4mJCSEH374gdGjRxusnZycHAIDA/H19WXv3r1m97nPzs5m+/btKBQKDh06hL29PX379kUul9OyZUuxmlSwWHK5nHv37rF8+XJuZ+bxR8IN9u8/wNHw76lV/03e/nYlVs/ov+J+28eaSe/g07QNg0e+T/t6/lhlpbFp0yZu3LjB+vXrjfRqDKPEJWt/la1UcyOjgDvZSu5kK7mbXUB+MVmbnRVUdrClioMNlR1sqOFoi4OFZ+F37txhzZo1hIeHExsbS5UqVRg2bBjDhw/Hz8/P1OEJwnPr3r07sbGxxMXFvdReZ89r5syZzJo1i5iYGHx8fAzWjj7cvHmTVatWoVAouHz5MjVr1mT48OEMHz4cLy8vU4cnCC9ELpezYkXhlhs2Nja4urpSJziYdj3706jHIO7lqp+r/36YcJHVi7/h5B9/kJ6eTvXq1WnTpg0ff/wx3t7eRn5V+lWik7W/kySJbKWEUpJQqcHaCmxkMhxsZGb3LVpfJEni/PnzhIeHs27dOlJTU2ncuDFyuZwBAwbg4uJi6hAF4R/98ccfNG/enHXr1jFw4ECDtZOcnIy/vz/jxo1jzpw5BmtH3yRJ4tSpU4SHh7NhwwYyMjJo2bIlcrmcvn37UrZsWVOHKAh68Tr233/1WiVrr7vc3FytMmnRvm5yuZy2bduKMqlgViRJonnz5mRlZXH+/HmDlvn69OnD6dOnSUhIsNgEJzs7m23btqFQKDh8+DAODg6aMmmLFi1EmVQQLJhI1l5TKSkpmjJpXFwcVatW1ZRJDXmEjyA8rz179tC1a1f27dtHx44dDdbOr7/+SocOHVi7di2DBg0yWDvGdOPGDU2Z9MqVK3h6ejJ8+HCGDRuGp6enqcMThBeyZs0a3n333WLvq1mzJpcuXTJyRMYnkrXXXHFnFzZp0kRTJnV2dn72RQRBz1QqFXXr1qV8+fIcOXLEYGWO/Px8QkJCqFixIr/99luJK6dIksSJEydQKBRs2LCBzMxMWrVqhVwup0+fPhY7iii8XjIyMrh3716x99na2hp8P0RzIJI1QSM3N5edO3eiUCg4cOAAdnZ2WmcXijKpYCyrV69m6NChnDp1isaNGxusnfnz5zNp0iQuXLhASEiIwdoxB1lZWVpl0jJlytCvXz/CwsJo3rx5iUtUBaEkEcmaUKzbt2+zevVqFAoF8fHxVKtWTVMmNfeVcoJly8/Px9fXl9DQULZt22awdu7cuYOvry/Dhw9n0aJFBmvHHF2/fp2VK1eiUCi4evUqXl5emjKph4eHqcMTBOFvRLImPJUkSZw9e1ZTJn3y5Alvvvkmcrmc/v374+TkZOoQhRJm0aJFjB8/nujoaAICAgzWzrBhw9i3bx+JiYm4uroarB1zJkkSf/zxBwqFgo0bN5KZmUnr1q0JCwujd+/elClTxtQhCoKASNaEF5CTk8OOHTtQKBQcPHiQ0qVLa5VJxWoz4VVlZGRQq1YtunbtyvLlyw3WzokTJ2jWrBm//PIL//rXvwzWjiXJyspiy5YtKBQKjh49StmyZenfvz9yuZxmzZqJMqkgmJBI1oSXcuvWLU2ZNCEhgerVq2s25bT0zQcF0/nyyy+ZNWsWly9fpkaNp58H+LJUKhUNGjTAxsaGM2fOiC8ZxUhOTtaUSa9du0atWrU0n29DvS+CIPwzkawJr0SSJM6cOUN4eDjr168nPT2dZs2aacqkjo6Opg5RsBAPHz7Ey8uLf/3rX3z77bcGa+enn35i9OjRnD59mkaNGhmsnZJArVbzxx9/EB4ezqZNm8jOzqZNmzbI5XJ69+6Ng4ODqUMUhNeCSNYEvcnJydGcXfjrr79ib29Pnz59kMvltGrVSoxgCE/10UcfsXTpUq5evUqFChUM0sajR4/w8fGhR48eBi2zlkSZmZmaMulvv/2Go6Ojpkz65ptvijKpIBiQSNYEg/j72YU1atTQlFFq1apl6vAEM3Pjxg1q167NtGnT+OyzzwzWzujRo1m7di2JiYlUqlTJYO2UdNeuXWPFihWsWLGC5ORkvL29kcvlDB06VJRJBcEARLImGFTR2YUKhYL169eTkZFBixYtNGcXijKpADBixAh2797N1atXDbZR64ULF2jQoAELFixg3LhxBmnjdaNWqzl+/DgKhYJNmzaRk5ND27Ztkcvl9OrVS5RJBUFPRLImGE12dramTHro0CHs7e01Zxe2bNlSlElfU7GxsQQFBfHdd98xduxYg7QhSRLNmjXjyZMnREREYGtra5B2XmcZGRmaMumxY8dwcnJiwIAByOVymjRpIsqkgvAKRLImmMTfzy6sWbOmpkzq5eVl6vAEI+rVqxeRkZEkJCRgZ2dnkDZWrVrFsGHDOHLkCK1btzZIG8L/JCUlsXLlSlasWMH169epXbu2pkxavXp1U4cnCBZHJGuCSUmSxMmTJzVnF2ZkZNCyZUtNmVScXViynT59miZNmrBq1Srefvttg7SRnp6Oj48PLVu2ZMOGDQZpQyieWq3m2LFjKBQKNm/eTE5ODu3atdOUSe3t7U0doiBYBJGsCWYjOzubrVu3olAoOHLkCA4ODpoyaYsWLUSZtISRJInWrVvz+PFjIiIiDHb27MSJE1myZAnx8fFiVMeEMjIy2LRpEwqFgt9//x0nJycGDhyIXC6ncePGokwqCE8hkjXBLF2/fl1TJk1KSsLT01NzdqGnp6epwxP0YP/+/XTq1Indu3fTpUsXg7QRFxdHcHAwM2bM4NNPPzVIG8KLu3LliqZMeuPGDXx8fDRl0mrVqpk6PEEwOyJZE8xacWcXtmrVCrlcTp8+fUSZ1EKp1Wrq169P2bJlOX78uEFGVSRJokOHDiQnJxMTE0OpUqX03obwatRqNUePHkWhULBlyxby8vJo3749crmcHj16iDKpIPyXSNYEi5GVlaVVJi1Tpgz9+vUjLCyM5s2bizKKBVm/fj2DBg3ijz/+4M033zRIG1u2bKFv374GHbkT9Cc9PV1TJv3jjz9wdnbWlEkbNWokPt/Ca00ka4JF+vvZhV5eXpoyqYeHh6nDE54iPz+fgIAAAgIC2Llzp0HayM7Oxt/fn6CgIHbv3m2QNgTDuXz5smbT3Vu3buHn54dcLuftt9+matWqpg5PEIxOJGuCRSs6u7CoTJqVlUXr1q0JCwujd+/elClTxtQhCn+zZMkS/v3vf3Px4kWCgoIM0sbnn3/O3LlzuXTpEt7e3gZpQzA8lUqlVSbNz8+nQ4cOmjJp6dKlTR2iIBiFSNaEEiMzM5OtW7cSHh7Ob7/9RtmyZTVnFzZr1kyUUcxAVlYW3t7etG/fnpUrVxqkjatXrxIQEMCECROYPXu2QdoQjO/Jkyds2rSJ8PBwTp48iYuLCwMHDiQsLIw33nhDfL6FEk0ka0KJdO3aNU2ZNDk5mVq1aiGXyxk2bJg4u9CEvvrqK7744gsSEhIMtqq3Z8+enD9/nvj4eDGyWkIlJiayYsUKVq5cya1bt/D399eUSatUqWLq8ARB70SyJpRoarWa33//XXN2YXZ2Nm3atEEul9O7d29xdqERPXr0CC8vL+RyOd9//71B2ijaDmTDhg3079/fIG0I5kOlUnHkyBEUCgVbt24lPz+ft956C7lcTvfu3UWZVCgxRLImvDYyMzPZvHmz5uxCR0dH+vfvT1hYGE2bNhVlFAObNGkSP/74I1evXqVixYp6v35eXh5BQUFUq1aNw4cPi/fzNZOWlsbGjRtRKBScOnUKV1dXBg0ahFwup0GDBuL3QbBoIlkTXktXr17VlEmvX7+Ot7e3ZlNOUSbVv1u3blG7dm0mTZrEjBkzDNLG3LlzmTp1KhcvXiQwMNAgbQiWISEhQVMmvX37NgEBAZoyqbu7u6nDE4QXJpI14bVW3NmFbdu21ZxdKMqk+jFy5Ei2b99OUlISTk5Oer/+7du38fX1ZeTIkSxYsEDv1xcsk0ql4tChQygUCrZt24ZSqaRjx47I5XK6desmNkoWLIZI1gThvzIyMjRl0uPHj+Pk5MSAAQOQy+U0adJElFFeUnx8PIGBgcyfP5/x48cbpI3Bgwdz+PBhEhMTcXZ2NkgbgmVLS0tjw4YNKBQKTp8+jaurK4MHD0Yul1O/fn3x+RbMmkjWBKEYSUlJmk05b9y4Qe3atTVlUnEY+Ivp168fZ8+eJSEhwSATvo8fP07Lli1Zvnw5YWFher++UPLEx8ejUChYuXIld+7coU6dOsjlcoYMGULlypVNHZ4g6BDJmiA8hVqt5rffftOUSXNzc2nXrh1hYWH07NlTnF34DH/++ScNGzYkPDwcuVyu9+srlUrq16+Pvb09J0+exMrKSu9tCCWXUqnUlEm3b9+OUqmkU6dOyOVyunbtKsqkgtkQyZogPKf09HQ2b95MeHg4f/zxB05OTpqzCxs3bizKKMVo164dd+7cISoqCmtra71ff/HixXzwwQecPXuWBg0a6P36wusjNTWVDRs2EB4eztmzZylXrhyDBw8mLCyMunXris+3YFIiWROEl3DlyhVNmfTmzZv4+PhoNt0VZxcWOnToEO3bt2f79u306NFD79d/8OABPj4+9OvXj59//lnv1xdeX7GxsaxYsYJVq1Zx584dgoKCNGXSSpUqmTo84TUkkjVBeAVqtZqjR48SHh7O1q1bycvLo3379pqzC1/XMqkkSbzxxhvY2dlx4sQJg4xKjBw5ks2bN5OYmIibm5very8ISqWSX3/9lfDwcHbs2IFKpaJz586aMqmdnZ2pQxReEyJZEwQ9KTq7UKFQcOLECZydnTVl0kaNGr1WZZRNmzbRv39/jh07RosWLfR+/T///JNGjRqxaNEi/v3vf+v9+oLwd48fP2b9+vUoFAr+/PNPypcvz5AhQ5DL5YSGhr5Wn2/B+ESyJggGcPnyZU2Z9NatW/j5+WlWk5b0swsLCgoIDAzE29ubvXv36v36arWaJk2akJuby/nz57GxsdF7G4LwNJcuXdKUSe/evUtwcLCmTGqI0zkEQSRrgmBAxZ1d2KFDB02ZtCSeXfjLL78watQoIiIiCA0N1fv1w8PDGTFihMFG7QTheSmVSg4cOIBCoWDnzp2o1Wo6d+5MWFgYnTt3FmVSQW9EsiYIRvLkyRPN2YUnT57ExcVFc3bhG2+8USLKKNnZ2dSuXZtWrVqxZs0avV8/LS0NHx8f2rdvb5DrC8LLevTokaZMeu7cOSpUqKBVJhWEVyGSNUEwgb+fXejv7685u9CSy6Tz5s1j6tSpxMfHU6tWLb1ff/z48SxdupSEhASx6lYwWzExMSgUClavXs29e/cICQnRlEnFYhjhZYhkTRBMSKVScfjwYc3Zhfn5+VpnF1pSmTQ1NRUvLy8GDx7MDz/8oPfrx8TEEBoayldffcWkSZP0fn1B0LeCggKtMqkkSXTt2hW5XE7nzp2xtbU1dYiChRDJmiCYieLOLiwqkzZo0MDsy6SffPIJCxcuJCkpSe9H9kiSRJs2bUhJSSE6OlrMBRIszqNHj1i7di0KhYILFy7g5ubGkCFDCAsLIzg42NThCWZOJGuCYIbi4+M1ZdKUlBQCAwM1ZVJzPLswJSUFb29vPvroI2bNmqX362/cuJEBAwawf/9+3nrrLb1fXxCMKSoqihUrVrB69Wru379P3bp1kcvlDB48mAoVKpg6PMEMiWRNEMyYSqXi0KFDhIeHa84u/GuZ1FzOLhw9ejQbN27k6tWrODs76/XaWVlZ+Pn5Ub9+fbZv367XawuCKRUUFLB//37Cw8PZtWsXMplMUybt1KmTKJMKGiJZEwQLUXR2oUKh4MyZM7i6umrOLqxXr57JyqSXL1/G39+fOXPmMHHiRL1ff+rUqcyfP5+4uDg8PT31fn1BMAcPHjxg3bp1KBQKIiIiqFixIm+//TZyuZygoCBThyeYmEjWBMECxcXFacqkd+7coU6dOprVZsYukw4cOJATJ06QmJio9+O1rly5QmBgIFOmTGHGjBl6vbYgmKuLFy9qyqQPHjygXr16yOVyBg0aJMqkrymRrAmCBSs6u1ChULB9+3ZUKhWdOnXSlEkNPRH/woUL1K9fn6VLl/LOO+/o/fpdu3YlOjqauLg4HBwc9H59QTBn+fn57Nu3D4VCwe7du5HJZHTv3h25XM5bb70lyqSvEavnfaBMJkMmk2Fra0ulSpVo3749y5cvR61W6z2otWvXYm1tzXvvvadzn1wup2fPnlq3JScnI5PJiIyM1Hssr6q4ePXliy++EJstvuZsbGzo1KkTGzZs4M6dOyxatIj79+/Tt29fqlSpwgcffMCFCxcw1HeyTz/9FD8/P4YPH673a+/evZs9e/awYMECkagJryU7Ozt69OjBtm3bSElJ4euvv+bKlSt069aN6tWrM3HiRGJiYkwdpmAEz52sdezYkTt37pCcnMy+ffto3bo148aNo2vXriiVSr0GtXz5ciZNmsT69evJzs7W23UlSSKrQE1anorHuSrS8lRkFagN1pEJgjGVK1eO0aNHc+bMGWJiYhgxYgSbNm2ifv36hISE8O2333Lv3j29tXf06FEOHDjA7Nmz9X4+Z25uLuPHj6ddu3b06tVLr9cWBEvk5ubGuHHjiIyMJCIigoEDB7JixQqCgoJo0KABixcv5tGjR6YO02Be+/5bek49evTQue3w4cMSIP3yyy+SJElSWlqaNHLkSMnNzU1ydHSUWrduLUVGRmo9Z8eOHVL9+vWlUqVKSeXLl5d69eqldf+1a9cke3t7KS0tTWrUqJG0YsUKzX3Tp0+XAK1/R48e1bmtZcuWkiRJ0pChw6Q2nbtJ706bLbm4VZIcnF2lxv3CpFlnUqT/u/BA829+5ANpTWKqdORWphT7OFfKKlBJkiRJjx8/loYOHSq5uLhI9vb2UseOHaXExESteEJCQrTiX7BggVSzZs2nxnvt2jUJkNatWyc1adJEKlWqlBQQECAdPXpUc53w8HDJ2dlZ69rbtm2Tit6y8PBwnWuHh4c/7S0UXkMFBQXSnj17pH79+kl2dnaSjY2N1L17d2nr1q1SXl7eS19XrVZLDRs2lN544w1JrVbrMeJCs2fPlmxsbKTY2Fi9X1sQSoq8vDxp27ZtUo8ePSQbGxvJzs5O6tu3r7R7926poKDA1OG9kqwClRT7OFc6citTWpOYKs2PfKDVbz+r/y5pXunrcJs2bQgJCWHr1q288847dOnShXLlyrF3716cnZ35z3/+Q9u2bUlMTKRcuXLs2bOH3r17M3XqVFatWkV+fj579uzRuuby5cvp0qULzs7OvP322yxbtoxhw4YBMHHiROLi4khPTyc8PBwoHE04e/YsDRs25NChQwQEBPBIacWu5HTi0/KIOf4b2Q7lCftpG49uXmPdlJG4+wbRsPdQTZv5ariRqeRWphI1hcON/q52LB4zjJtXk9i5cydOTk5MnjyZzp07Exsb+1xzBf4p3pSUFAA+/vhjvvvuOwICAvj222/p3r07165do3z58s+89oABA4iJiWH//v0cOnQIQO9bJgiWz8bGhs6dO9O5c2ceP36sWW3Wu3dvypcvrzm7sG7dui903e3bt3P27FkOHz6s91WoN2/eZPbs2YwbNw5/f3+9XlsQShI7Ozt69uxJz549uX//PmvWrCE8PJyuXbtSuXJlzWrSwMBAU4f6XCRJIiVbyYUHOcSl5mv646dNtvqn/ruemz1VHGzMfjPx5/XcZdB/4ufnR3JyMkePHiU6OppNmzbRoEEDateuzTfffIOLiwubN28GYPbs2QwcOJAZM2bg7+9PSEgIn376qeZaarUahULB22+/DRSuMjt16hRXrlwBoGzZstjb21OqVCkqV65M5cqVsbOz05y1lmnnyJ600ux+aE1saj4SYO/oQrfJc6joWRv/Fh3wa96OpLPHi30t6r/87/GLcRzcs5se076lcnBDQkJCWLNmDbdv337uvZ7+Kd4iY8aMoU+fPvj7+7NkyRKcnZ1ZtmzZc13b3t6esmXLYmNjo7m2vlfiCSVLuXLl+Pe//82ff/5JdHQ0YWFhbNiwgXr16hEaGsp3333H/fv3n3kdpVLJ1KlTad++PW3atNF7nBMnTsTJyYnPP/9c79cWhJKqYsWKfPjhh1y8eJELFy7Qv39/wsPDqVOnDg0bNuTHH3/k8ePHpg7zHyWm5bEsPo1ViU+I/W+iBk9P1P7qr4+PTc1nVeITlsencflJnv6DNYFXTtYkSUImk3H+/HkyMzMpX748ZcuW1fy7du0aSUlJAERGRtK2bdt/vNbBgwfJysqiU6dOAFSoUIEOHTqwfPnyp8aQqyx8mw7fzuJRrqowrv/eV6mWL1bW1prHOlaoRObjh898XfeuXcbKxgYnn7psuZrBjuR0HJxd8fX1JS4u7pnPfx5NmjTR/LeNjQ0NGjTQ27UF4Wnq1KnD119/za1bt9i1axfe3t5MmjSJqlWr0rNnT7Zv305+fn6xz125ciVxcXH83//9n97jOnLkCBs3bmTevHk4OTnp/fqCUNLJZDLq1q3L999/T0pKClu3bsXd3Z0PPvgAd3d3+vfvz969e/U+1/xl5SjV7EhOZ+u1DJ3++2UVPf9hrkrTf+co9b8Y0pheeVZw0UaVarUad3d3fvvtN53HuLi4ADxz5Gf58uU8fvxYa+WXWq0mIiKCL7/8Euu/JF1FEtPyWHv5ieb///1Ntrb5e7lShiQ9x5v230mLEiAD4lPzSU5PJVep1gyrWllZ6UxuLCgoePa1n8KQ1xaEv7OxsaFr16507dqVR48eacqkvXr1okKFCpoySkhICFA48X/69On079+f+vXr6zWWgoICPvjgA5o2baoZXRcE4eXZ2dnRq1cvevXqxb179zRl0i5duuDu7s7QoUMZPnw4AQEBJokvMS2PfTcyyVX9r7/Vp6LrFfXfnWqUxcfFPE59eVGvNLJ25MgRoqOj6dOnD/Xq1ePu3bvY2Njg7e2t9a9oE7/g4GAOHz5c7LUePXrEjh07WL9+PZGRkVr/MjMz2bdvH1D4y6dSqZAkiZN3s9l6LQOldWFCJqn0lzlX9PJBrVRyM+Z84bWBh48ekXTlMlbuXkiShJubG3fv3tVKqv6+fUhRvMU5ffq05r+VSiXnz5/Hz88PKFz5k5GRQVZW1ktdWxBeVPny5RkzZgznzp0jKiqKYcOGsXbtWkJDQzXf1OfNm8edO3f48ssv9d7+Dz/8QGxsLIsXLy4x80wEwVxUqlSJjz76iKioKM6fP0/fvn1ZunQpgYGBNGrUiCVLlpCammqUWP7af+eoJL0naTrtATkqia3XMjh1N9siV5A+d7KWl5fH3bt3uX37NhcuXOCrr76iR48edO3alWHDhtGuXTuaNGlCz549OXDgAMnJyZw8eZJp06Zx7tw5AKZPn866deuYPn06cXFxREdHM2/ePABWrVpF+fLl6devH3Xq1NH8Cw4OpmvXrpq5XB4eHkRFRbHq90j2xd5EVVBAGdcK2Ja2J/HkYTIe3Sc3I/2VfzAVatQioFUntn75EckRp7mTGMPGaaNxcqsMwW04fiebli1b8uDBA+bNm0dSUhI//PCDJqksUhRvQkICDx8+1Bod++GHH9i2bRvx8fH8+9//JjU1lREjRgDQqFEjHBwc+PTTT7ly5Qpr165FoVDoXPvatWtERkby8OFD8vJKRm1eML2goCDmz5/PrVu32LlzJ15eXnz88cdMnz6d6tWrEx8fr9eR3nv37jF9+nTefffdF17sIAjC85PJZNSrV4+FCxeSkpLC5s2bqVixImPHjsXd3Z0BAwawf/9+gw0ESJLEsTvZHL+jv225XkRR25aWsD13srZ//37c3d3x8PCgY8eOHD16lIULF7Jjxw6sra2RyWTs3buXFi1aMGLECHx8fBg4cCDJyclUqlQJgFatWrFp0yZ27txJaGgobdq04cyZM0BhCbRXr15YWemG1KdPH3bv3s29e/cYOXIklTy8GdmpObPa+nH94lmsbWzo9vFszmxdyf+9FcTKj4bqXONl9P1iIVX9Q1gxbghL5J2RJAn5onVY29py6l4OT8p58OOPP/LDDz8QEhLC2bNndc5GHDlyJL6+vjRo0AA3NzdOnDihuW/OnDnMnTuXkJAQfv/9d3bs2KEZhSxXrhyrV69m7969BAUFsW7dOr744gudn0vHjh1p3bo1bm5urFu3Ti+vWxCK2Nra0q1bN7Zs2cLYsWOxsbGhbNmy9OjRg6pVq2q+qb+qTz75BBsbG2bNmqWHqAVBeB6lSpWiT58+7Nq1i1u3bjF79mwuXbpEp06dqFGjBlOmTNH7POpT93I4fS9Hr9e0xBhelMUdN5WYlsfWaxmmDkOjt6fjC9fAk5OT8fT0JCIiQpxAIFiEe/fu4eXlxdixY5kzZ47O2YV169ZF/v/s3XdcU9f7B/BP2FtQFHCAsodM9xb33ntxsXZZR9WqddVttXW1am1dBMW9965bcQKCbBAXoCIgeyQ5vz/8cn+mQWUEksDzfr14fb/NvffkSSKcJ+e55xyOw6hRo0q9d2FgYCBatWqFzZs3F7trCSGk8jDG8PDhQwiFQuzZswdpaWlo0aIFOI7DiBEj+HvQ/ysxMRFmZmbF3ltepCr034qiUslarkiCLeFpyBUrT8i66gJ842wCXY2S3/5HyRpRNZMnT0ZAQADi4+NhYmLCP15YWMjvXXjy5EkIBAL07dsXHMehR48eX1yPUCwWo0WLFpBIJLh///5n/9ATQipXfn4+Tp48CaFQiLNnz0JTUxMDBw4Ex3Ho0qUL//v65s0bWFpaomfPnjh48GCxO5pUlf5bUVQqWTuekIHI/62fVl5PH92BcPKITx5ffOtZidoR4MMCfP0alnyZAUrWiCqJj4+Ho6Mjli5ditmzZ3/yvLdv3/L3VgYHB6NOnTr8bFJXV9dir9m6dSu++eYb3Lp1C61bt66ol0AIKaekpCQEBARAKBQiPDwcdevWxbhx48BxHM6cOYMZM2YA+LAf9vbt22UmCcmz/5aXsvTfiqIyyZq8h08L83Lx/k3SJ4+bWlqXqr3B1oawq6Eaw6mElMaYMWPw77//IjY2tsQbqgcHB/Nl0pSUFDRp0gQcx2HkyJH8Dh2pqamwt7dH79694e/vX5EvgRAiJ4wxPHjwgC+TpqenQ1dXF7m5/38P2IwZM/D777/zCZuylT//SxX6b5VI1hhj2B6Zjnd5YqXKyosIAJjqqGO8ozEtOUCqlJCQEHh6emLz5s349ttvS319QUEBzpw5A6FQiNOnT0MgEKBfv378t/GAgABER0fD3Ny8AqInhFSkvLw8/PHHH/j5559lji1fvhxz586l/ltOyr0obmVIzBEhJU951xNjAN7miZGYI0I9/S/vGUqIqpg7dy5sbW35JWVK6797F+7Zswd+fn7o27cvAKBDhw5ISUmhZI0QFaSjo4NXr15BTU0NEon0Oqfz5s1DTk4Ovp+zkPpvOVD+u+oAPHqbC+XNdz8Q4EOchFQV169fx5kzZ7Bs2bIvThQoiTp16uDHH39EcHAwPD09YWJigidPnsDV1RXNmjXDpk2blHrvQkKIrNOnT0slajo6OrCwsEDdunVhZGRE/becKH0ZNEckwcbQ1BJv5qpIagAmudaEngrMLCHkcxhjaNu2LfLy8nD//v1i1z8sqz179mD06NG4ePEi2rdvj9OnT/NlUnV1dfTr1w++vr7o1q1bsbPKCCHK49GjR0hJSUG9evVQr1491KhRgy8nUv8tP0qfrEWk5eN4gvLemPhfAxoawtFEuW9UJORLTpw4gf79++PChQvo2rWr3NrNzMyEo6MjWrVqhUOHDkkd+3jvwrCwMJibm2Ps2LHgOE5hexcSQsqO+m/5Uc4U8iPJOSLlD/J/1PAhXkJUmVgsxty5c9GpUyd06dJFrm0vW7YMaWlpWLNmjcyx/+5dOHToUGzfvh0uLi5o3rx5pe5dSAgpP+q/5Ufp38eknEKVGEIFAAk+TIYgRJXt3r0bT548wa+//irX2VFRUVFYt24d5syZAysrq0+e99+9Cw8fPgwzMzNMnjwZ5ubmGD58OM6ePQuRiH7XCFFm1H/Lj1KXQRljWPf4HQpU5dMGoKUmwDS3mko9BZiQT8nPz4eDgwOaNGmCw4cPy61dxhh69uyJ6OhohIeHQ0dHp9RtJCcn82XSJ0+ewMLCgi+TOjk5yS1WQkj5Uf8tX0o9spYjYir1QQNAgYQhR6S0+S8hn/X333/jxYsXWL58uVzbPXHiBM6fP4/169eXKVEDAHNzc8yYMQOhoaF48OABBg8ejG3btsHZ2RktW7bE33//TWVSQpQE9d/ypdTJWqFEOd+0LxEp72AlIZ+UmZmJZcuWwdfXF46OjnJrNzc3Fz/++CN69OjBr69WHgKBAE2aNMGGDRuQmJiIgwcPwtTUFJMmTYKFhQVGjBiBc+fOQSxW3rWdCKnqqP+WL6VO1lT0s4ZYxb5NEAIAa9euRWZmJhYuXCjXdn///Xe8evUKf/zxh9zLC9ra2hgyZAhOnTqFFy9eYNmyZQgNDUXPnj1haWmJOXPmIDIyUq7PSQj5Muq/5UupkzU15Ssbl4i6Ur+rhMh68+YNVq9ejcmTJ6NBgwZyazchIQG//vorpk+fDnt7e7m1WxwLCwv89NNPCAsLw/379zFgwAD8888/cHJyQqtWrfDPP/8gPT29QmMghHxA/bd8KWlYH2iq6KetoYQ3JxLyOStWrICamlqxe/yVx4wZM1CzZk3Mnz9fru1+jkAgQNOmTbFp0yYkJibiwIEDqFmzJiZOnAgLCwuMGjUKFy5coDIpIRWI+m/5UupkTU9DAC2ljlCWOhNDkpet6DAIKbGEhARs3rwZs2bNQq1ateTW7sWLF3HkyBGsXr0aBgYGcmu3NHR0dDB06FCcPn0aL168wJIlSxAcHIzu3bvDysoKc+fORVRUlEJiI6QqU8X+W0tNAD0N5UzWlHrpDgDYE5OO51nKu/bJxxiTIP7BLWz/bjDs7Ozg6ekJDw8P/oc2qybKiOM4nDt3DnFxcdDX15dLmwUFBXB3d0edOnVw9epVpZoKzxjD/fv3IRQKsXfvXqSnp6NVq1bgOA7Dhw9HjRo1FB0iIVWCKvXfAGBpoIlRdsr5+6/0ydqVV9m4/yZXJRbWUwNgyd4jJ/gagoOD+Z/MzA/bbZiZmUklbx4eHrCzs4O6urpiAyfVVlhYGNzc3LBx40ZMnDhRbu2uWbMGs2bNQlBQENzc3OTWrrzl5eXhxIkTEAqFOH/+PLS0tDBo0CBwHIdOnTrR7yYh5aBq/XfzOrroWE8+X1jlTemTNVXfW0wikSAhIUEqeQsODsaLFy8AALq6unBzc4OHhwc/Eufq6go9PT1FvQRSjfTv3x9hYWGIiIiAlpaWXNpMSkqCvb09OI7Dhg0b5NJmZXj16hUCAgIgFAoRGRmJ+vXrY9y4cfDx8anwyRGEVEWq3n8rE6VP1nJEEmwMTVWZzHySa03oaXy5UJ+SkoKQkBCpBC4iIgJisRhqamqwt7eXGYUzMzOr+BdBqo3bt2+jTZs22L17N0aNGiW3dseNG4ezZ88iOjoaJiYmcmu3sjDGcO/ePb5M+v79e7Rp0wYcx2HYsGEwMjJSdIiEqISq2n8rgtInawBwMiED4WkFUOZABQCcTbTQt2HZ/5Dn5eXhyZMnMqNwWVlZAD6s4P7xCJyHhwdsbW2hpqac/7iI8mKMoUOHDsjIyMCjR4/k9m/o1q1baNu2LbZu3YoJEybIpU1Fys3NxfHjxyEUCnHhwgXo6OhIlUnpd4+Qz6su/XdFU4lk7VV2IXZFv1d0GF801r4G6ulryrVNiUSCp0+fIjg4GEFBQXwC9+rVKwCAvr4+X0Yt+mncuDGVUclnnTlzBr1798aZM2fQs2dPubQpFovRtGlTaGho4O7du1UukXn58iVfJo2KikKDBg3g4+MDHx8f2NraKjo8QpRSde6/5UklkjXGGHZEpiMlT6yU2bkAgKmOOsY7GlfarLe3b9/KlFEjIyP5MqqDgwOfvHl6evIz8wiRSCTw9PSEsbGxXGdq/v333/j+++8RGBiIFi1ayKVNZcQYw927d+Hn54d9+/YhIyMDbdu2BcdxGDp0KJVJCfkI9d/yoRLJGgDEvM/H4XjlvVFxsLUh7Goo9sbE3NxcqTJqUFAQQkJCkJ39Yd23unXrytwHZ2NjU+VGQMjn7d69G2PGjMHt27fRqlUrubT57t072Nvbo3///tixY4dc2lQFubm5OHbsGIRCIS5evAgdHR0MHjwYvr6+6NixI/1uEQLqv+VBZZI1ADiekIFIJat9CwA4mWihn5LWuiUSCeLi4mTug0tMTATwoYzq7u4uU0bV1dVVcOSkIhQUFMDR0RFubm44duyY3Nr9/vvvsXfvXkRFRVXbiTAvX77Erl274Ofnh5iYGFhaWvJlUhsbG0WHR4hCVfX+m+M4pKeny/Xv6sdUKlnLFUmwJTwNuWLlCVlXXYBvnE2gq6QzSD7lzZs3xZZRJRIJ1NXV4ejoKDMKZ2pqquiwSTlt2rQJkydPRmhoKFxcXOTS5qNHj9C0aVOsW7cOU6dOlUubqowxhjt37kAoFGL//v3IyMhAu3bt+DKpoaGhokMkSq6iO/6y6tixI65du4a9e/dixIgR/OPr16/H+vXrkZCQ8MlrK7L/XtXbC+lJL6QeM6pjgTnnHn/2+JKLofjG2QROttZ49uwZ7ty5g5YtW/Ln/PjjjwgODsbVq1e/GMP79+/BGIOxsXG5X09xNCqk1Qqiq6GGnpYGOPJUeYZTe1oaqFyiBgB16tRB165d0bVrV/6xnJwchIWFSSVwR48eRU5ODgCgXr16MgmctbU1lXpURFZWFpYuXYpx48bJLVGTSCSYNGkSXFxc8MMPP8ilTVUnEAjQunVrtG7dGuvXr+fLpBMmTMDkyZMxZMgQcByHDh060O8OUQkFBQX8Oow6OjqYP38+Bg8eDE3Nkt+Q/6X+W1xYCPVStPdfXb7/Gc0HjuH/W/CfBa2LO/5x/62jo4PZs2fj2rVrZXr+it75ROX+Utgba6O9hXLMdOxgoQd7Y+Wuc5eGnp4emjdvjm+++QZ//fUXbt++jYyMDERFRWH//v0YN24cJBIJduzYgaFDh8LOzg7GxsZo164dJk+ejO3bt+Phw4fIy8tT9EshxVi/fj3S0tKwePFiubUZEBCAO3fuYMOGDdDQUKnvfpVCT0+P3zg+ISEBc+fOxe3bt9GpUydYW1tj4cKFiI+PV3SYRIWsXbsWrq6u0NfXR4MGDTBx4kR+eafs7GwYGRnh0KFDUtecPHkS+vr6/G46r169wvDhw2FiYoJatWqhf//+UqNiHMdhwIAB+PXXX1G3bl2pRaFHjhyJ9+/fY+vWrZ+Nc/PmzbCxsYGWlhYcHBywa9cuqf57jldt3D0kxM5pY/FLayv8u20tLv39G/4c0REPju3Gyl4eWNjGCsdWzIRELMY14QYs7+qMZZ2dcGXbWpnn09bTh6GpGf9jYGL62eO9nS2l+u9vv/0WgYGBOHPmTAk+BVlF71mRjh07YsqUKZg1axZq1qwJc3NzLFq0SOoagUCAzZs3l6h9lUvWAKCVmS5amSn2nqpWZrpoqeAYKoO6ujrs7e0xbNgwrFixAmfOnEFiYiKSk5Nx7tw5zJs3D/Xr18elS5fw9ddfo2nTpjAwMICrqyvGjh2LNWvW4PLly3j37p2iX0q1lpKSgt9//x0TJ06ElZWVXNrMyMjArFmzMGzYMHTs2FEubVZllpaWmDdvHqKjo3Hz5k107doV69atg42NDTp27AihUMh3uoR8ipqaGv7880+EhYXB398f//77L2bNmgXgwz3II0aMgJ+fn9Q1fn5+GDJkCAwNDZGTkwNvb28YGBjg+vXruHnzJgwMDNCjRw8UFBTw11y+fBkRERG4ePEiTp06xT9uZGSEuXPnYsmSJfzktf86evQopk6dihkzZiAsLAzffvstfH19ceXKFan++9Lfv8G5Qw/8eOAamvb/sDD3u5cJiLp9Gb4b92PEii14cHwPhFNG4v2bRHyz9QR6TFmAC3/9iuePH5T5PSyu/27YsCG+++47zJkzBxKJfJbx9ff3h76+Pu7evYvffvsNS5YswcWLF6XOWbBgQYnaUslkTSAQoL2FHjooaIStg4UeOtTVV+ppvhXNzMwM3bt3x+zZs7F3715EREQgMzMTgYGB2LRpE9q2bYvY2Fj88ssv6NKlC0xNTWFpaYl+/frhl19+wZEjRxAfHw8VumVSpa1cuRKMMcydO1dubS5evBiZmZlYvXq13NqsDgQCAdq0aYOtW7ciOTkZu3btgoaGBsaPHw9zc3NwHIerV6/KrcMgVcuPP/4Ib29vNGrUCJ06dcLSpUtx4MAB/viECRNw/vx5fhJZSkoKTp06hfHjxwMA9u3bBzU1NWzbtg2urq5wcnKCn58fnj9/LnVvlr6+PrZt2wYXFxc0btxYKoaJEydCR0cHa9fKjnABwOrVq8FxHCZOnAh7e3tMnz4dgwYNwurVq/n+GwDcewxC0wGjUbN+Q5jUbQAAYBKGIQv/hJm1A5w6dId10zZIeRaHPj8tR+2GtmjafxRqN7RF/MNbUs957s+lWNjGiv+5tXdLsceXtG2I3vZmxW6FN3/+fDx9+hS7d+8uyUfxRW5ubli4cCHs7Owwbtw4NG3aFJcvX5Y6Z+jQoSVqS2XrFgKBAK3M9VBLRx1nn2chT8wqdJaJAICOugA9LQ2qVOlTnvT19dGiRQupNbbEYjFiY2OlFvX9559/8ObNGwAfvqX9dzaqi4sLtLXpPZaX58+fY+PGjZg7dy5q164tlzYjIiLw559/YvHixWjQoIFc2qyO9PT0MGbMGIwZMwbPnj3Drl27IBQK4e/vj0aNGsHHxwfjxo1Do0aNFB0qURJXrlzBihUrEB4ejoyMDIhEIuTl5SE7Oxv6+vpo3rw5XFxcsHPnTvz888/YtWsXLC0t0b59ewDAw4cPERsbKzPRJS8vD3Fxcfx/u7q6fnK/YG1tbSxZsgSTJk3C999/L3M8IiIC33zzjdRjbdq0wR9//AEA/EDHgA4toakukOq/Teo2gLa+AX+dQa3aUFNXl7q/06BmbWSlpki1337cD/Dq+/+THvRNakkd78RNwszvvkIjow+vqbgJc7Vr18ZPP/2EX375BcOHDy/2tZeGm5ub1H9bWFjwfV+Rki6fpLLJWhF7Y200MNDEhZdZiEgrgACQa9JW1J6TiRa61lfNyQSKpK6uDgcHBzg4OEj9409OTpaayHDhwgVs3LgRjDFoaGjAyclJamstd3d31KxZU4GvRHUtXrwYRkZGmDZtmlzaY4xhypQpaNiwIWbMmCGXNglgZWWF+fPnY968ebh16xaEQiFWr16NRYsWoWPHjuA4DoMHD4aBgcGXGyNV0rNnz9CrVy989913WLp0KWrWrImbN2/iq6++QmFhIX/ehAkTsHHjRvz888/w8/ODr68vnyBJJBI0adKk2NGjj7/M6evrfzaWMWPGYPXq1Vi2bBkaNmwoc/y/lSfGmMxjtnWM0d3ZhO+/AUD9P/e+CgQCqGv8Z+KBQAD2n5FnPeOaMLW0lj4NH/pvLTUBujrUR1cv58++JgCYPn06/vrrL/z1119fPPdL/jsBQyAQlHnEvEpkHroaaujf0AiDGhnCVOfDDJDyFiiLrjfVUcdga0P0a2hEiZocmZubo0ePHvj555+xb98+REZGIjMzk79ZvXXr1oiKisLcuXPRqVMn1KpVC1ZWVujfvz8WLlyIo0eP4unTp1RG/YKIiAgIhULMnz9fbktGHDlyBJcuXcL69etpBLQCCAQCtG3bFtu2bUNycjJ27twJNTU1cBwHc3Nz+Pr64vr161QmrYYePHgAkUiENWvWoGXLlrC3t+fLnR8bM2YMnj9/jj///BNPnjyBj48Pf8zLywsxMTGoU6cObG1tpX5KM6NRTU0Nv/76KzZv3iyzZIeTkxNu3rwp9djt27fh5OQk087H/beexoeeV979t66GAJrqJWvVwMAACxYswPLly5GRkVHOSL4sMDCwROep/Mjax+yNtWFXQwuJOSI8epuLiLQCSPAhIy3Jn7Wi89QEgLOxNrxq68BCT6Na35tWmfT19dGyZUupdW5EIhFiYmKkRuE2b96Mt2/fAvgwXfrjMqqnpyecnZ0/OXxf3cyfPx+Wlpb49ttv5dJeTk4Opk+fjj59+qB3795yaZN8mr6+PsaOHYuxY8ciISGBL5MKhUJYW1vzZdLiRjaIanv//j2Cg4OlHqtduzZEIhE2bNiAvn374tatW/j7779lrjUxMcGgQYMwc+ZMdOvWDfXr1+ePjR49Gr///jv69++PJUuWoH79+nj+/DmOHDmCmTNnSp37Jb1790aLFi3wzz//SC2GPXPmTAwbNgxeXl7o3LkzTp48yX/J+xR7Y214muogTlsdziZafP9d0t636Dx59N/ffPMN1q1bh71791b41nkHDx4s0ShelUrWgA/fSuvpa6KeviY615fgeWYhknJESMoRITmnEAXFZG1aaoC5nibq6mnAXE8Dloaa0KNRNKVQVBJ1cnLCyJEjAXwYTv9vGfXcuXPYsGEDGGPQ1NSEs7Oz1H1w7u7uMDExUfCrqVx3797FkSNHsHPnTrmNgK1cuRLJyckyN8mSitewYUMsWLAA8+fPx40bNyAUCvHbb79h4cKF8Pb25sukXypfEdVw9epVeHp6Sj3m4+ODtWvXYtWqVZgzZw7at2+PX3/9FePGjZO5/quvvsKePXv4iQVF9PT0cP36dcyePRuDBg1CZmYm6tWrh86dO5dpX9tVq1ahdevWUo8NGDAAf/zxB37//XdMmTIFjRo1gp+f3xdnjQsEAmiqCdC3oRHff1/V0UBKjgBaavhk/62hBlgZamFAQ0O59N+amppYunQpRo0aVa52SqKkSymp1A4G5cUYQ46IQcQYxBJAXQ3QEAigpyGg0bMqICsrC48fP5ZK4kJDQ/l136ysrGQW9bWysqqSnz1jDJ06dUJKSgqCg4Oh/p8FIssiPj4ezs7O+Omnn7Bs2TI5REnKKysrC0eOHIFQKMSVK1dgYGCAYcOGgeM4tG3btkr+2yYls3v3bkydOhWJiYlVotJQFftvgUCAo0ePSq3P9slzq1OyRqofkUiE6OhoqQQuKCgIKSkfZhIZGxvLJHBOTk4q/8ftwoUL6N69O06cOIG+ffvKpc3+/fsjKCgIERERNHqjhBISEuDv7w9/f388ffoUNjY2fJlUXmvrEeWXk5ODp0+fYvjw4ejfvz+WL1+u6JDIJ1CyRshnMMaQlJQktZxIcHAwYmNjAXwYAndxcZFJ4ip6OxF5kUgkaNq0KfT09HDjxg25fOs8d+4cevbsif3792PYsGFyiJJUFIlEwpdJDx48iOzsbHTq1Am+vr4YNGgQ9PSUYwcYUjEWLVqE5cuXo3379jh+/DjNHpaTz72PZ8+eRbt27UrdJiVrhJRBZmZmsWXU/Px8AECjRo1kErgGDRoo3RD8/v37MWLECNy4cQNt27Ytd3v5+flwdXVF/fr1cfnyZaV7veTTsrKycPjwYfj5+eHatWswNDTky6Rt2rShz5KQEir6Ml+cevXqQVe3Ync0omSNkM8QiUSIioqSKqEGBwfz22eZmJgUW0YtzQbH8lRYWAhnZ2c4ODhIbRFTHqtWrcK8efMQEhIitw3gSeWLj4/Hzp074e/vj4SEBNja2vJlUktLS0WHRwj5DErWCCklxhhevXolNQIXHBzMr/6tpaWFxo0bSyVwbm5ulVJG/fvvvzFx4kQEBwfLrJ5dFq9evYKDgwO+/vprrFu3Tg4REkWTSCS4fv06/Pz8cOjQIeTm5qJz587gOA4DBw6kMikhSoiSNULkJCMjo9gyatHmyNbW1jKjcPXr15dbKSonJwc2Njbo0qULdu3aJZc2R40ahcuXLyM6Olpl7tkjJZeZmYlDhw5BKBTi+vXrMDQ0xPDhw8FxHFq3bk1lUkKUBCVrhFSgwsJCREZGyozCpaamAgBq1qwpta2Wh4cHHBwcylRGXblyJX755RdERkbC2tr6yxd8wfXr19GhQwf4+fmB47hyt0eUW1xcHF8mffbsGezs7MBxHMaOHUv7vxKiYJSsEVLJGGN4+fKlTAIXHx8P4MMmycWVUT+3YGVaWhqsra0xZswYbNiwodwxikQieHl5QV9fH7du3ZLaRJlUbRKJBFevXoVQKMShQ4eQl5eHLl268GXSir6RmhAii5I1QpTE+/fvERISIpXAhYWF8Rs029jYSG2r5eHhgbp160IgEGD27NnYtGkT4uLipLZ9KauNGzdiypQpuHfvHpo2bVru9ohqysjI4MukN27cgJGREUaMGAGO49CyZUsqkxJSSShZI0SJFRQUFFtGTUtLAwCYmprCyckJt2/fRt++fbFs2TI4ODhAQ6PsO8m9ffsW9vb2GDp0KLZs2SKvl0JUXGxsLL/o7osXL2Bvb8+XSUuznyQhpPQoWSNExTDG8OLFC34pEaFQiOfPn0Mi+bBxno6ODl9GLRqBc3NzK/HimF9//TUOHTqE6Oho1K5duyJfClFBEokEV65cgVAoxOHDh5Gfn4+uXbuC4zj079+fyqSEVABK1ghRYVFRUXBxccHvv/8OX19fmTLqkydPUFhYCIFAAFtbW5nZqBYWFlKlrPv376NFixbYsGEDfvjhBwW+MqIKMjIycPDgQQiFQty8eRM1atTgy6QtWrSgMikhckLJGiEqbNiwYQgMDER0dDR0dHRkjhcUFCAiIkKmjJqeng4AqF27ttQkht9++w0A8OjRo3KVUkn1ExMTw5dJX758CQcHB75MWq9ePUWHR4hKo2SNEBX14MEDNGvWDDt27ICvr2+Jr2OM4dmzZzIJ3LNnzwB8mI3q7u4uNQLn6upKewySEhGLxVJl0oKCAnTr1o0vkxb3pYIQ8nmUrBGiorp164ZXr17h8ePHUFdXL1db6enpfJm0T58+UmVUkUgEgUAAOzu7YsuohHzK+/fvceDAAQiFQty+fRvGxsZ8mbR58+ZUJiWkhChZI0QFXb58GV26dMHRo0cxYMCAcrf3448/Yvv27YiKikLdunX5x/Pz8xEeHi4zCpeRkQEAMDMzk0ng7Ozsyp08kqonOjoa/v7+2LlzJ16+fAknJydwHIcxY8ZI/ZsjhMiiZI0QFcMYQ/PmzaGuro47d+6Ue3QiLCwMHh4eWLFiBWbNmlWi509ISJBJ4J4/fw4A0NXVhZubm0wZVV9fv1xxkqpBLBbj8uXLEAqFOHr0KAoKCtC9e3dwHId+/fpRmZSQYlCyRoiKOXz4MIYMGYIrV66gY8eO5WqLMYZOnTohMTERoaGh0NLSKnNbqampCAkJQVBQEJ/AhYeHQywWQyAQwN7eXmZrLXks4EtUV3p6Ol8mvXPnDkxMTDBy5EhwHIemTZtSmZSQ/6FkjRAVIhKJ0LhxYzRq1Ahnz54td3sHDhzA8OHDce7cOXTv3l0OEUrLy8srtoyamZkJADA3N5cpo9ra2lIZtRqKioqCUCjEzp07kZiYCGdnZ75MSvdGkuqOkjVCVMi2bdvw9ddf49GjR/D09CxXW9nZ2XB0dESTJk1w7Ngx+QRYAhKJRKqMWjQS9/LlSwCAnp4e3NzcpEbgGjduDD09vUqLkSiOWCzGpUuX+DJpYWEhevToAV9fX/Tt2xfa2tqKDpGQSkfJGiEqIjc3F3Z2dmjXrh327t1b7vbmzZuHNWvWICIiAo0aNZJDhOWTkpIis6hvREQExGIx1NTU4ODgIDMKV6dOHUWHTSpQeno69u/fDz8/P9y9excmJiYYNWoUOI5DkyZNqExKqg1K1ghREatXr8acOXMQEREBW1vbcrUVGxsLFxcX/Pzzz1i8eLGcIpS/vLw8PHnyRKaMmpWVBQCwsLCQ2dzexsYGampqCo6cyFtERAQ/mzQpKQkuLi58mdTc3FzR4RFSoShZI0QFpKenw9raGiNGjMBff/1V7vb69OmDsLAwhIeHq1x5USKRID4+XiaBe/XqFQBAX19fZlHfxo0b056VVYRIJOLLpMeOHYNIJELPnj3BcRz69OlDZVJSJVGyRogKmDdvHtatW4e4uLhy32x96tQp9O3bF4cPH8agQYPkFKHivX37lp+NWlROjYiIgEQigZqaGhwdHWXKqLRRvWpLS0vDvn37IBQKce/ePdSsWZMvk3p5eVGZlFQZlKwRouSSkpJga2uLqVOnYsWKFeVqKy8vj59NeuHChSrfmeXm5iIsLExqBC4kJATZ2dkAgHr16skkcNbW1lRGVUHh4eF8mTQ5ORmurq7gOA6jR4+mJWKIyqNkjRAlN3HiROzbtw/x8fEwNjYuV1vLly/HokWL8PjxYzg5OcknQBUjkUgQFxcnU0ZNTEwEABgYGBRbRqXFWlWDSCTCxYsX4efnh+PHj0MsFqNXr158mbQ8awkSoiiUrBGixGJjY+Hk5IQVK1Zg5syZ5WrrxYsXcHR0xPfff4/Vq1fLKcKq482bN3z5tGg5kaioKEgkEqirq8PJyUkqgXN3d4epqamiwyafkZqaypdJ79+/j1q1amH06NHgOA4eHh5VfmSZVB2UrBGixEaNGoXr168jJiam3DfIDx8+HNevX0dUVBSMjIzkFGHVlpOTU2wZNScnBwBQv359mTJqo0aNqIyqhJ48eQJ/f3/s2rULycnJcHNz48uktAQMUXaUrBGipIKCguDl5YWtW7diwoQJ5Wrr33//RefOnbFz506MHTtWThFWT2KxWKaMGhQUhOTkZACAoaEh3N3dpRb1dXFxoVmKSkIkEuH8+fMQCoU4ceIEJBIJevXqBV9fX/Tq1YvKpEQpUbJGiJLq2bMn4uPj8eTJE2hoaJS5ncLCQnh6esLY2Bg3btyg0k8FSU5OllnUNyoqCowxaGhoFFtGrVWrlqLDrtbevXvHl0kfPHgAU1NTqTIpIcqCkjVClNDVq1fh7e2NgwcPYsiQIeVqa/369Zg+fToePnxY7i2qSOlkZ2dLlVGDgoLw+PFj5ObmAgAaNGhQbBmVEurKFxoaypdJ37x5A3d3d75MSku8EEWjZI0QJcMYQ+vWrSESiXDv3r1yddyvX7+Gvb09Ro8eLZfFdEn5icVixMTEyJRR37x5AwAwMjKSSeCcnZ2pjFpJCgsLpcqkjDH06dMHHMehV69e0NTUVHSIpBqiZI0QJXPs2DEMHDgQly5dQufOncvVlq+vL06cOIHo6GgquSm55ORkmeVEoqOj+TKqs7OzTBm1Zs2aig67Snv37h327NkDoVCIR48eoXbt2nyZ1N3dXdHhkWqEkjVClIhYLIarqyvq1auHixcvlqutwMBAtGrVCps3b8Z3330npwhJZcrKykJoaKhUAvf48WPk5eUBACwtLaX2RfXw8ICVlRWVUSvA48eP4e/vj4CAALx58wYeHh7w9fXFqFGjaAkXUuEoWSNEiQiFQvj6+uL+/fto2rRpmdsRi8Vo0aIFJBIJ7t+/D3V1dTlGSRRJJBIVW0Z9+/YtAKBGjRrFllFplqN8FBYW4ty5c/Dz88PJkychEAj4MmnPnj2pTEoqBCVrhCiJvLw82Nvbo2XLljhw4EC52tq6dSu++eYb3Lp1C61bt5ZThERZMcaQnJzML+Zb9BMTEwMA0NTUlCqjenp6wt3dvdw7YlR3b9++xd69eyEUChEUFITatWtjzJgx8PX1haurq6LDI1UIJWuEKIl169Zh5syZePLkCRwcHMrcTmpqKuzt7dG7d2/4+/vLMUKiajIzM2XKqKGhoXwZtWHDhjKjcJaWllRGLYOQkBC+TPr27Vt4eXmB4ziMHDmSyqSk3ChZI0QJZGRkwNraGoMGDcKWLVvK1dakSZOwc+dOREdHw9zcXE4RkqpCJBIhOjpaalut4OBgpKSkAACMjY1lEjgnJycqo5ZQQUEBzp49C6FQiFOnTkEgEKBv377gOA49evSgMikpE0rWCFECCxcuxG+//YbY2FjUq1evzO2EhITAy8sLv//+O6ZPny7HCElVxhhDYmKizGzU2NhYAICWlhZcXFxkZqPWqFFDwZErt7dv32LPnj3w8/NDSEgIzMzMMGbMGHAch8aNGys6PKJCKFkjRMFev34NGxsb/PDDD1i1alWZ22GMoUOHDkhJSUFISAh9gyfllpmZicePH0tNZAgLC0N+fj4AoFGjRjKjcA0aNKAyajGCg4MhFAqxe/dupKSkoEmTJnyZlJbVIV9CyRohCjZlyhTs2rULcXFx5Vo3a8+ePRg9ejQuXryILl26yDFCQv5fYWEhoqKiZGajpqamAgBq1qwpk8A5OjrSl4f/KSgowJkzZyAUCnH69GmoqamhX79+4DgO3bt3L9fWcqTqomSNEAV6+vQpHBwcsGTJEvz8889lbiczMxMODg5o3bo1Dh06JMcICfkyxhhevXolU0aNi4sD8KGM2rhxY5kyqpGRkYIjV6w3b95g9+7d8PPzQ2hoKMzMzDB27FhwHAcXFxdFh0eUCCVrhCjQ2LFjcfnyZcTGxkJPT6/M7cyePRsbNmxAREQErKys5BghIWX3/v17qTJqcHAwwsLCUFBQAACwtraWWk7Ew8MD9erVq3ZlVMaYVJn03bt3aNasGTiOw4gRI2inCkLJGiGK8vjxY3h4eOCvv/4q1w4DUVFRcHV1xYIFC7BgwQI5RkiI/BUWFiIyMlKmjJqWlgYAqFWrlkwZ1cHBodqUUQsKCnD69Gm+TKquro7+/fuD4zh069aNyqTVFCVrCnL16lV4e3sjLS2NFqaspvr27YvIyEiEh4eXuSNijKFnz56Ijo5GeHg4dHR05BwlIRWPMYYXL17IlFGfPn0KANDW1i62jGpoaKjgyCvW69ev+TJpWFgYzM3N+TKps7OzosMjlahUyRrHcfwim+rq6qhbty569+6NFStWwMTEpMKC/JTbt2+jXbt26Nq1K86dO1fpz18elKxVbzdv3kS7du2wb98+DB8+vMztHD9+HAMGDMDx48fRr18/OUZIiOKlp6cXW0YtLCwEANja2sqMwtWtW7fKlVEZYwgKCuLLpKmpqWjevDlfJlVE/0sqV6mTtdevX8PPzw8ikQjh4eEYP3482rVrh71791ZknMWaMGECDAwMsG3bNoSHh8PS0vKz5zPGkCNiKJQwSBigJgA01QTQ0xBU+i83JWvVF2MM7dq1Q05ODh48eAA1NbUytZObmwtnZ2c4OjrizJkzVa6DIqQ4BQUFiIyMlNlaKz09HQBgampabBm1qpQP8/PzcerUKQiFQpw9exYaGhp8mbRr165V5nX+lzL13wrBSsHHx4f1799f6rHp06ezmjVrMsYYE4vFbPHixaxevXpMS0uLubu7s7Nnz/LnPn36lAFge/fuZa1atWLa2trM2dmZXblyRarNJ0+esJ49ezJ9fX1Wp04dNmbMGPb27Vupc7KyspihoSGLjIxkw4cPZ4sXL5Y6fuXKFQaAbd53lFk7NWaaWtrMsrEXm7r/Gvv10Vv266O3bMiiP5mOgRHj1voz84Y2TFNbm7Xq0IlFxSdItXXixAnm5eXFtLW1WaNGjdiiRYtYYWEhfxwA27p1KxswYADT1dVltra27Pjx41JtnD59mtnZ2TEdHR3WsWNH5ufnxwCwtLS0Er33fn5+rEGDBkxXV5cNGDCArV69mtWoUaNE1xLlcvLkSQaAnTt3rlztLF68mGlqarKoqCg5RUaIapJIJCwhIYEdO3aMLVq0iA0YMIA1bNiQAWAAmI6ODmvWrBn7+uuv2aZNm9itW7dYZmamosMut6SkJLZ69Wrm4uLCALC6deuy2bNns/DwcEWHVm7ZhWIWnprH/n2ZxXZHp7E1wW/5vvvjnzXBb9nu6DT278ssFp6ax7ILxYoOvUKUK1mLi4tjzs7OzMzMjDHG2Nq1a5mRkRHbu3cvi4yMZLNmzWKamposOjqaMfb/yVr9+vXZoUOHWHh4OJswYQIzNDRkKSkpjDHGEhMTmampKZszZw6LiIhgjx49Yl27dmXe3t5SsWzfvp01bdqUMfah82vYsCGTSCRMIpGwl1kFbPneUwwAq9PInn3110E2df815tiuGzOpa8mW3U3kkzV1DU1Wz9mDfed3hk0OuMTqN/ZiVu7N2Imn79nLrAJ29uxZZmRkxIRCIYuLi2MXLlxgDRs2ZIsWLfr/N/F/r2nPnj0sJiaGTZkyhRkYGLB3794xxhh7/vw509bWZlOnTmWRkZEsICCAmZmZlThZCwwMZAKBgP36668sKiqK/fHHH8zY2JiSNRUkEolY48aNmbe3N5NIJGVu5+nTp0xHR4fNnj1bjtERUrWkpqayq1evsvXr1zMfHx/m7u7ONDQ0GAAmEAiYnZ0dGzp0KFu+fDk7ffo0e/XqVbl+LxVFIpGwBw8esB9++IGZmJgwAKxFixZs8+bNLDU1VdHhlVhR/33i6Xu26n/J2KpiErTifj4+v6j/VsXP8lNKXQYNCAiAjo4OxGIxvxnw2rVrMW3aNNSrVw8//PAD5s6dy1/TvHlzNGvWDJs2bUJCQgIaNWqElStXYvbs2QA+7FPXqFEjTJ48GbNmzcIvv/yCu3fv4vz583wbL1++RIMGDRAVFQV7e3sAQJs2bTBs2DBMnToVIpEIFhYW+H2rP0QOrZCSJ0b8g1vY+s0AjPx1C9y6DwQA5LxPw8qe7hiy6E+4dRuAhyf24tCiKfje/xwsXZsAAN48jcG6wa3xw87zqN/YCzu+7oe+vXtizeL/n2UXEBCAWbNmITExEQAgEAgwf/58LF26FACQnZ0NQ0NDnDlzBj169MDcuXNx7NgxPHnyhB+u/fnnn7Fq1aoSlUFHjRqFtLQ0nD17ln9sxIgROHfuHD/0T1TDrl27MG7cOAQGBqJFixZlbmfw4MEIDAxEVFQUDAwM5BghIVVbfn4+IiIiZCYzvH//HgBQu3ZtqRKqp6cn7O3toa6uruDISyY/Px8nT57ky6SampoYMGAAfH190aVLF6V9HdHp+bielIOUPDEE+DAkWlZF19fWUUf7unqwq6EtnyAVqNTFbW9vb2zevBk5OTnYtm0boqOjMXnyZGRkZCAxMRFt2rSROr9NmzYICQmReqxVq1b/H4CGBpo2bYqIiAgAwMOHD3HlypViO6C4uDjY29sjKioK9+7dw5EjRwAAhVBD854Dsf6f7Ri5ornUNZZuzfj/r1fDBKZWNnjzNIZ/TE1DA/WdPfj/rtPIDjqGNfD6aTTqN/ZCwpMQ/BkWhL9Wr4L6/8riRYlqTk4OvzaWm5sb34a+vj4MDQ3x5s0bAEBERARatmwpVVf/+D34koiICAwcOFDqsVatWqncpIrqLj8/H7/88gsGDhxYrkTt4sWLOHLkCPbs2UOJGiGlpK2tzSdiRRhjePbsmVTytn//fvz+++8AAF1dXbi6ukolca6urkr5+6etrY0hQ4ZgyJAhSEpKQkBAAIRCIXr06IG6deti3Lhx8PHxgaOjo6JDBQDkiiS48DILEWkFKOohy7tERdH1KXliHI7PhJNJPrrVN4CuRtnuD1YGpU7W9PX1YWtrCwD4888/4e3tjcWLF2PmzJkAIHOjH2OsRDf/FZ0jkUjQt2/fYvdItLCwAABs374dIpGI3/Ca/e951DU0kZORDl0j4xI910cPfPIcxiTo8u0sNO7UG9rqAnSoq49GRloAILVMwn+XXhAIBJBIJP9ro3z/9Mp7PVEOW7ZswfPnz3HmzJkyt1FQUIApU6agffv2GDFihByjI6T6EggEaNiwIRo2bIgBAwbwj6empiIkJIRP4AIDA7Fjxw6IRCIIBALY2dnJTGYo6qeUgYWFBWbOnImffvoJDx48gFAoxN9//42VK1eiZcuW8PX1xbBhwxQ2yS06PR9nn2chT/yhj5N3T1fUXmRaARIy0tDT0gD2xqo5ylbuaSMLFy5Ez5498f3336Nu3bq4efMm2rdvzx+/ffs2mjeXHu0KDAzkzxGJRHj48CEmTZoEAPDy8sLhw4fRsGHDYme1iEQi7Ny5E6tXr4ZZk/Z48DaPP7Z7pi+CzhxC6xET+Meehz6AsUV9AEBuRjpSnsWjdkNb/rhEJMKr8GA0aOwFAHibEIu8zPeo3dAOAFDP0RUpz2JRy9IaAPAQgIGBHlqa6ZZ4BoqzszOOHTsm8x6UlLOzs8z5pbmeKF5mZiaWLl0KjuPg5ORU5nY2bNiA6Oho7N+/v3rMgCJEgWrWrAlvb294e3vzj+Xn5yM8PFxqFG7lypXIyMgAAJiZmckkcHZ2dgotPwoEAjRr1gzNmjXDmjVrcPLkSfj5+eH777/H1KlTMXDgQHAch86dO1dKnIwx3Hmdi+tJORX+XMCHpC1XzHDkaSY6WIhL1X8ri3Inax07doSLiwtWrFiBmTNnYuHChbCxsYGHhwf8/PwQHByM3bt3S12zadMm2NnZwcnJCevWrUNaWhrGjx8PAPjhhx+wdetWjBw5EjNnzoSpqSliY2Oxb98+bN26FadOnUJaWhoceo9EWK4WzGv8f7uNO/fFg+O7pZK1f7eugV6NmjCoVRsXNq2AvnFNOHv34o+ra2jixG9z0HfmCqhraODEqjlo4NqUT946ff0T/H8cjRpm9eDatR8EAjVsOP8EfyXFYOf6VSX6wL/77jusWbMG06dPx7fffouHDx9CKBSW+D2eMmUKWrdujd9++w0DBgzAhQsXqASqYtatW4eMjAwsXLiwzG0kJSVh0aJFmDhxolTZnRBSebS1teHp6QlPT0/+McYYnj59KpXA7dmzh68Q6erqws3NTaaMqq+vX+nx6+joYOjQoRg6dCgSExP5Mmn37t1Rr149jBs3DhzH8feHyxtjDNeSchD4OrdC2v+Sa0k5KJAwtLfQU6mETS4LskyfPh2+vr6Ijo5GRkYGZsyYgTdv3sDZ2RknTpyAnZ2d1PkrV67EqlWrEBQUBBsbGxw/fhympqYAgLp16+LWrVuYPXs2unfvjvz8fFhZWaFHjx5QU1PD9u3b0aSdN8JytWTiaNy5D67uWI9XEf9/j1z3yQtwavU8pDyPh4W9C8at3wUNzf+/VlNHFx18JmP/vO/w/nUiGnq0wOCFf/DH7Vt3gs/63fh362pc37kRahoaqN3QDs0GjEHg61y0Mv/yfo6WlpY4fPgwpk2bhr/++gvNmzfHihUr+AT1S1q2bIlt27Zh4cKFWLRoEbp06SI1oYEot7dv32L16tX44YcfvrgW4OfMnj0bOjo6WLJkiRyjI4SUl0AggLW1NaytrTFo0CD+8Xfv3kmVUW/fvo1t27ZBLBZDIBDA3t5eal9UDw8PmJmZVVrcdevWxaxZszBz5kzcv38fQqEQmzdvxq+//orWrVuD4zgMGzYMNWrU+HJjJXTnda7CErWPY9BSE5So/1YWlbrdVNFs0KCgIKmbO0sjOj0fR55mfvG8otmgv1yLha5h8f/QHp7Yi1Or52Ph9bgyxQIAgxoZKqQGLhQK8eOPP9JsUBUwffp0bN++HfHx8ahVq1aZ2rh16xbatm2LrVu3YsKECV++gBCilPLy8hAeHi61qG9ISAgyMz/0a+bm5jJlVFtb20oro+bl5eH48eMQCoW4cOECtLS0MGjQIHAch06dOn0yjsLCwi9um1fS/ruyKKr/LguVWuo4VyTB2edZig5DytnnWWhgoKnSs0xIxXn27Bk2bdqEBQsWlDlRE4vFmDRpEpo1a1bi0VhCiHLS0dGBl5cXvLy8+MckEolMGTUgIAArV64EAOjp6UmVUT09PdG4cWN+NQJ5xzd8+HAMHz4cr169QkBAAPz8/LBnzx40aNCAn036ccUsIyMDNjY2GDNmDNauXVtseZH67/JRqWTtwsv/nzWiLPLEDBdfZqFfQ6Myt9GzZ0/cuHGj2GNz586VWreOqJZFixbBxMQEP/74Y5nb2LJlCz8TraxbUxFClJeamhpsbGxgY2ODwYMH84+npKQgJCSEH4W7ceMGtm7dCrFYDDU1NTg4OMiMwtWpU0ducdWrVw+zZ8/GrFmzcO/ePfj5+WHjxo1Yvnw52rRpw5dJDxw4gJSUFKxfvx4mJib45ZdfZNqqqv13ZanUMmh5KNvw6X8NtjYs88J7r169Qm5u8TX8mjVrombNmuUJjSjIkydP4Obmhj///BM//PBDmdp49+4d7O3t0b9/f+zYsUPOERJCVE1ubi6ePHkiNQoXEhKCrKwPo1YWFhbFllHl9UUvNzdXqkyqo6MDfX19vHv3jl9mauPGjVJ/86py/11ZVCJZY4xhe2Q63uWJ5b4OizwIAJjqqGO8o7FKzS4hFWvAgAF4/PgxIiMjoaUlOyGmJL777jvs27cPUVFRlXrjMSFEdUgkEsTHxyM4OFjqXriiXXb09fXh7u4ulcA1btwYurq65Xrely9fYv369VizZo3Msd27d2PUqFHUf8uJSpRBE3NESMkTKzqMT2IA3uaJkZgjQj39z99gSaqHO3fu4Pjx4wgICChzovbo0SNs2bIF69ato0SNEPJJampqsLW1ha2tLYYMGcI//ubNG6nZqFevXsXff/8NiUQCNTU1ODo6yozC1a5du8TPW79+fWRmZkJNTY1fBL7I6NGjkZmZiT5jxlP/LQcqMbJ2MiED4WkFSpmVFxEAcDbRQl8VqH2TisUYQ8eOHZGeno6goKAylR8kEgnatm2LzMxMBAUFFbtANCGElFZubi7CwsL4BC4oKAiPHz9GdnY2gA/LeXy8lIiHhwesra0/+XdMU1MTIpFI6jE1NTVoamri22+/RZdpS6n/lgOl7wFyRBJEKPkHDXzIziPSCtC5vgR6KjCzhFScc+fO4fr16zh9+nSZ7xMJCAjAnTt3cOXKFUrUCCFyo6ury+9mUEQsFiMuLk7qPrgdO3YgKSkJAGBgYFBsGTUvL08qURs+fDh+++031K9fH2pqasgRSbAxNJX6bzlQ+pG1iLR8HE9Q3hsT/2tAQ0M4mij3jYqk4kgkEnh5ecHIyAjXrl0r0z0QGRkZsLe3R8eOHbFv374KiJIQQr7s9evXUmXU4OBgREVFQSKRQF1dHQ0aNEBCQgJ/vkAggKenJ44cOQIrKyvqv+VI6b+yJ+eIoAZA8sUzFU8NH+JV1g+bVLz9+/cjJCQEt27dKvPNqosXL0ZmZiZWr14t5+gIIaTkzMzM0K1bN3Tr1o1/LCcnB6GhoQgODsauXbukkjXGGB49egRbW1vs2bMHpq17Uf8tJ8o53veRpJxClfiggQ//IBNzRF88j1RNBQUFmD9/Pvr164fWrVuXqY3w8HD8+eefmD9/PurXry/nCAkhpHz09PTQokULfPvtt7Czs+Nv9RAIBPwXVHV1dbx584b6bzlS6pE1xhiSlfjNK05yjgiMMaWeAkwqxrZt2/D06VMcP368TNczxjBlyhQ0bNgQ06dPl3N0hBAiX+Hh4ZBIJNDQ0EDr1q3Rp08f9OzZEy4uLgCAdY/fKTjC0lHm/lupk7UcEUOBqqTl/1MgYcgRMehrKt+HTSpOdnY2lixZgrFjx6Jx48ZlauPIkSO4fPkyTp06BW1t5RyKJ4SQIqtXr8a7d+/QuXNnGBoaSh3LLpRQ/y1HSp2sFUqUeu7DJ4mUe84GqQB//PEH0tLSsHjx4jJdn5OTg+nTp6NPnz7o3bu3nKMjhBD5a9eu3SePUf8tX0qdrKnoZw2xin2bIOXz7t07rFq1Ct9//z0aNmxYpjZWrlyJ5ORkXL58Wb7BEUKIAlD/LV9KPcFATflGIktEXanfVSJvK1euhEQiwdy5c8t0fXx8PH777TfMnDkTtra2co6OEEIqH/Xf8qWkYX2gqaKftoYS3pxIKsbLly+xYcMGzJgxA3Xq1ClTG9OmTUOdOnUwZ84cOUdHCCGKQf23fCl1GVRPQwAtNajUTYqaAgY9DeX8sIn8LV68GIaGhpgxY0aZrj979ixOnDiBAwcOQF9fX87REUKIYqhi/62lJlDa/lupkzWBQABzPQ08z1KN5TsYkyDy/i3U6/UDvyVH0R5rNjY2Zd56iCinyMhI7NixA2vXrpWZCVUS+fn5mDp1Kry9vaU2XyaEEFWnav03AJjraSjlsh2ACmw3deVVNu6/yVWJhfUEYKiR9hwvLh/ht+ZITEwEAOjr6xe7t5qurq6CoyZlNWTIEDx48ABRUVFlWmpj1apVmDdvHkJCQvh1iQghpKpQpf5bDUDzOrroWE85KxxKPbIGfMh0VeGDBgAGATp6NoZjpyb8Y2/evJHaW+3q1av4+++/IZFIoKamBkdHR6kROHd3d9SuXVuBr4KUxP3793H48GH4+/uXKVF79eoVli5dismTJ1OiRgipklSp/5bgQ7zKSulH1nJEEmwMTVWND5xJ8IOLCQy1NT97Wk5ODsLCwqQ2xw0JCUFOTg4AoF69elIjcB4eHrC2tqYyqpJgjKFLly78Jsfq6uqlbmPUqFG4fPkyoqOjUaNGjQqIkhBCFEuV+m81AJNca0JPQzn7WeVNI/9HT0MNTiZaCE8rgDJnlRKxCMFnj8Cx73KMGzcOHMfBwcGh2HP19PTQvHlzNG/enH9MLBYjLi5OKoHbsWMHkpKSAAAGBgZSZVRPT0+4uLhAR0enUl4f+X+XLl3Cv//+i+PHj5cpUbt+/Tr27t0LPz8/StQIIVWWqvTfAgBOJlpKm6gBKjCyBgCvsguxK/q9osP4ombiJBwV/oM9e/YgLS0NrVq1AsdxGDZsGIyNjcvU5uvXr6USuODgYERFRYExBnV1dTg5OcmMwtWqVUu+L4zwJBIJmjdvDm1tbdy8ebPUN6OKRCJ4eXlBX18ft27dotFSQkiVpir991j7Gqin//mqmCKpRLLGGMOOyHSk5ImVMjsXADDVUcd4R2MIBALk5+fjxIkTEAqFOHfuHLS0tDBw4EBwHIfOnTuXaTTmY9nZ2TJl1MePH/Nl1Pr168skcI0aNaLEQA4OHDiA4cOH4/r165/dauVTNm7ciClTpuDevXto2rRpBURICCHKQ9X6b2WlEskaAMS8z8fh+ExFh/FJg60NYVdD9kbzpKQkBAQEwM/PDxEREahfvz7GjRsHHx8f2Nvby+35xWIxYmNjERwcjKCgIP5/37x5AwAwMjKSmY3q4uJCG4aXQmFhIVxcXGBnZ4fTp0+X+vq3b9/C3t4eQ4cOxZYtWyogQkIIUT6q2n8rE5VJ1gDgeEIGIpWs9l1U6+7X0Oiz5zHG8ODBA/j5+WHv3r1IT09H69at+TJpRd27lJycLFNGjY6OBmMMGhoaxZZRa9asWSGxqLotW7bgu+++Q1BQENzd3Ut9/ddff43Dhw8jOjoapqamFRAhIYQoJ1Xuv5WBSiVruSIJtoSnIVesPCHrqgvwjbMJdEtxY2JeXh5fJj1//jy0tLQwaNAg+Pr6wtvbu9xl0i/Jzs5GaGgon7wFBQXh8ePHyMvLAwA0aNCAX0qk6Kdhw4ZKPURc0XJycmBnZwdvb28EBASU+vr79++jRYsW2LBhA3744YcKiJAQQpTXl/rv9ORXuPzPb4i6dRk56akwNDWDs3dPdPr6J+gbl3wAIfjsYRxYMBHNBo7FwHmrP3tuWfpvRVGpZA0AotPzceSp8gynDmpkCHvjsg+fJiYm8mXSyMhINGjQgC+T2tnZyTHSzxOJRIiJiZEagQsKCsLbt28BADVq1JApozo7O1ebMuqqVaswf/58REVFwdraulTXSiQStGrVCnl5eXj48CE0NJR+EjYhhMjdp/rv1JcJ2Mz1gqmlNbr+MAc161rhdXwkzq5fDFFhASb6n4NeDZMSPce27wajvosH7h4UYs75UGjp6n3y3PL235VJ5ZI1ALidnIPrSTmKDgMdLPTQyvzT/xBKgzGGe/fuQSgUYu/evXj//j3atGnDl0mNjCp/mJYxVmwZNSYmBowxaGpqwtnZWSqBc3d3h4lJyX6pVEVaWhqsra0xevRobNy4sdTX+/n5Yfz48WWelEAIIVVFcf2336TheB0XiRlHA6Gp8/+7+mSmvMbv/ZrDq88wDJj7+xfbTkt8jnVD2mLu+TDs+GEYWg7zhVef4cWeK8/+uzKoZLLGGMP1pBzceZ2rsBhamemivYVehZQG8/LycPz4cQiFQly4cAHa2toYPHgwOI6Dt7e3wmd1ZmVl4fHjx1IJXGhoKF9GtbKykrkPzsrKSmXLqHPmzMGGDRsQFxcHMzOzUl2bnp4Oe3t7dO3aFbt3766gCAkhRDX8t//OeZ+GZZ0c0O2Hueg4/keZ848snY6wyyex4Er0F/uQi5tX4s3TaIz+bQdu79uGsEsn8M22EzLnVWT/XVFUsh4jEAjQ3kIPWmoCXFPACFtFZ+Q6OjoYPnw4hg8fjlevXmHXrl0QCoUICAiApaUlXya1tbWtsBg+x8DAAK1bt0br1q35x0QiEaKjo6USuE2bNiElJQUAYGxsDHd3d6l74ZycnKClpaWQ11BSiYmJ+OOPPzBjxoxSJ2oAsGjRIuTm5uL337/8rZAQQqq6//bfKc/jwRhD7UbFr45Qp5E9cjPSkZ2WAoOan96KUSKR4OHJfeg361cAgHv3gTiz9hekPI+HqeX/37qiaiNqRVRyZO1j0en5OPs8C3liVqGzTAQAdNQF6GlpoJAaN2MMd+/ehVAoxL59+/D+/Xu0bdsWvr6+GDp0KAwNDSs9pi9hjCEpKUlqOZHg4GDExsYCADQ1NeHi4iJTRi3rAsIV4bvvvsPBgwcRHx9f6hm7YWFh8PDwwIoVKzBr1qwKipAQQlRTdHo+Np+6hvVju2PMGn+4ePeSOedGwGacWfsL5l2OgIHJp2fRR926jP3zv8fcC2HQ0PwwCCCcMgoWds7oMXm+QvtveVD5ZA34MMvkwsssRKQVQADINWkras/ZRAtd6xsoxayR3NxcHDt2DEKhEBcvXoSuri5fJu3YsaPCy6RfkpmZWWwZNT8/HwDQsGFDqW21PDw80KBBg0ofso6JiYGTkxNWrVqFGTNmlOpaxhg6deqExMREhIaGKv0IIiGEKMLL129haWGGrhPnoNNX02T675KWQffM/gqhF09A7aPVFJhEAkNTMwjvhKOHVQ2l6L/Lqkoka0Wi0/NxIykHb/PE5U7aiq6vraOO9nX1lHbBvJcvX/Jl0ujoaFhaWsLHxwc+Pj6wsbFRdHglJhKJEBUVJTUTNTg4GO/evQMAmJiYyNwH5+TkBE3NitseZMSIEbh16xZiYmJKvQfr/v37MWLECJw7dw7du3evoAgJIUT1de/eHSFhT7Dw1D28hxbf/5Z0gkF2eip+7e6KYUs2oo6NI3+9saYAa3x6Y+/u3ejTp08lvZqKUaWSNeDDiEZijgiP3uYiIq0AEgBqACQluLboPDUB4GysDa/aOrDQ01CJmxAZYwgMDOTLpBkZGWjfvj04jsOQIUOUskz6JYwxvHr1SmY2alxcHABAS0ur2DKqPBYYfvjwIZo2bYrt27dj/Pjxpbo2KysLTk5OaNKkCY4dO1buWAghpCqLiYlB69at4eTkhKnzFuG9oTluPQrF6T8WQ1yQj++/sHTHzd1/47r/Rvx87jE01NWk+u8xY8YgJycHR48ercRXJH9VLln7WI5IgueZhUjKESEpR4TknEIUFJO1aakB5nqaqKunAXM9DVgaakJPhYdLc3Jy+DLppUuXoKuriyFDhoDjOHTo0EHpy6RfkpGRUWwZtaCgAABgbW0tMwpXv379UiXd3bt3x/PnzxEaGlrqddHmzp2LtWvXIiIiAo0aNSrVtYQQUh09e/YMixYtwrlz5/Du3TuYmZujY8++GDx5NnK0a3y2/14/vAO8WrbBynUbZPrvI0eOYPjw4Xj58mWZJokpiyqdrP0XYww5IgYRYxBLAHU1QEMggJ6GQCVGz8rixYsXfJk0JiYGDRs2hI+PD8aNG1fqxV2VWWFhISIjI2VG4VJTUwEANWvWlErePD094eDgUGwZ9d9//0Xnzp1x5MgRDBw4sFRxxMbGwsXFBT///DMWL14sl9dGCCHVXXXsvz9WrZK16owxhtu3b0MoFGL//v3IzMxEhw4d+DKpgYGBokOUO8YYXr58KZPAxcfHAwC0tbXRuHFjqSTO1dUV3bp1AwAEBgaW+o9Anz59EBYWhvDwcOjpqd70cEIIIcqHkrVqqKh+7+fnh3///Rd6enoYOnQoOI5Du3btVL5M+iXv379HSEiIVAIXFhaGwsJC/pz27duja9eufBJXr169LyZup06dQt++fXH48GEMGjSool8GIYRUCzdu3EDPnj0/eTwrK6sSo1EMStaquWfPnvFl0ri4ODRq1Igvk1an+60KCgoQFhaGPn36QEtLC9bW1ggODkZaWhoAwNTUVOY+OAcHB/5+try8PDRu3BjW1tY4f/58tRiWJ4SQypCbm4tXr1598riiFoivTJSsEQAfSoa3bt3iy6RZWVnw9vYGx3EYPHgw9PX1FR1ihduxYwe++uorPHz4EF5eXmCM4cWLFzKL+iYkJAD4UEZ1dXWFh4cHkpOTcfbsWdy9exdNmjRR7AshhBBSpVCyRmRkZ2fjyJEjEAqF+Pfff2FgYCBVJq2Ko0a5ubmwt7dHmzZtsG/fvs+em56eLlVGvXfvHsLDw/njtra2Ugv6enh4wMLCokq+b4QQQioeJWvks549e4adO3dCKBQiPj4e1tbW/KK7VlZWig5PbtasWYOff/4Z4eHhsLOzK9W1w4YNw/Xr13H48GHExsZK3QuXnp4OAKhdu7ZMGdXe3r7Uy4IQQgipfihZIyXCGMPNmzfh5+eHAwcOIDs7G506dQLHcRg0aJBKl0nfv38Pa2trDBs2DJs3by7VtUXLfOzcuRNjx46VOsYYw7Nnz2Rmoz579gwAoKOjw5dRi0bhXF1dq+TMXEIIIWVHyRoptaysLL5MeuXKFRgYGGDYsGHgOA5t27ZVuXLf/PnzsXbtWsTGxqJu3bolvq6wsBAeHh4wMTHBjRs3Svy609LSZGajPnnyBCKRCAKBAHZ2djKjcObm5ir3vhJCCJEPStZIuSQkJPBl0qdPn8LGxgYcx2HcuHGwtLRUdHhflJycDBsbG0yZMgW//vprqa5dv349pk+fjocPH8LT07NcceTn5yM8PFxmFC4jIwMAUKdOHZnN7e3s7KD+0abFhBBCqiZK1ohcSCQS3LhxA0KhEAcPHkROTo5UmVRZF4idNGkS9uzZg/j4eBgbG5f4utevX8Pe3h6jR4/GX3/9VSGxMcaQkJAgk8A9f/4cAKCrqws3NzeZRX1VuSRNCCFEFiVrRO6ysrJw+PBhCIVCXL16FYaGhhg+fDg4jkPr1q2VppwXFxcHR0dHLF++HLNmzSrVtb6+vjhx4gSio6NRq1atCoqweKmpqQgJCZFaTiQ8PBxisRgCgQD29vYyW2up8p54hBBS3VGyRipUfHw8du7cCX9/fyQkJMDW1hYcx2Hs2LEKL5OOHj0aV69eRUxMTKlG/gIDA9GqVSv8/fff+PbbbyswwpLLy8srtoyamZkJADA3N5e5D87W1pbKqIQQogIoWSOVQiKR4Pr163yZNDc3F507d4avry8GDBhQ6WXS4OBgeHp64p9//sE333xT4uvEYjFatGgBxhju3bun1MmORCKRKqMWjcS9fPkSAKCnp1dsGVVZS9aEEFJdUbJGKl1mZiYOHToEoVCI69evw8jIiC+TtmrVqlLKpL1790ZsbCyePHlSqrXOtm7dim+++Qa3bt1C69atKzDCipOSkiIzGzUiIgJisRhqampwcHCQGYWrU6eOosMmhJBqi5I1olBxcXF8mfTZs2ews7PjZ5PWr1+/Qp7z+vXr6NChAw4cOIChQ4eW+LrU1FTY29ujd+/e8Pf3r5DYFCUvLw9PnjyRKaMWbZBsYWFRbBlVTU1NwZETQkjVR8kaUQoSiQRXr16FUCjEoUOHkJeXh65du4LjOAwYMAC6urpyeR7GGNq0aYOCggLcu3evVMnGpEmTsHPnTkRHR8Pc3Fwu8SgziUSC+Ph4mQSuaENlfX19uLm5SW2r1bhxY7l9VoQQQj6gZI0onYyMDL5MeuPGDRgZGWHEiBHgOA4tW7YsV5n0xIkT6N+/Py5evIguXbqU+LqQkBB4eXnh999/x/Tp08v8/FXB27dviy2jSiQSqKmpwdHRUWYUrnbt2ooOmxBCVBYla0SpxcbGwt/fH/7+/njx4gUcHBz42aT16tUrVVtisRju7u4wNzfHpUuXSnwdYwwdOnTg7/XS1NQs7cuo8nJzcxEWFiaVwIWEhCA7OxsAULduXakROA8PD1hbW1MZlRBCSoCSNaISJBIJrly5AqFQiMOHDyM/P58vk/bv379EpTd/f39wHId79+6hWbNmJX7uPXv2YPTo0aUejavuJBIJ4uLiZMqoiYmJAAADAwO4u7tLJXCNGzeGjo6OgiMnhBDlQskaUTkZGRk4ePAghEIhbt68iRo1amDkyJHgOA7Nmzcvtkyan58Pe3t7NGvWDIcOHSrxc2VmZsLBwQGtW7cu1XXk0968eSNTRo2MjIREIoG6ujpfRi0aiXN3d4epqamiwyaEEIWhZI2otJiYGL5M+vLlSzg6OvJl0o83Zf/jjz8wY8YMPHnyBA4ODiVuf/bs2diwYQMiIiJgZWVVES+BAMjJySm2jJqTkwMAqF+/vsx9cI0aNaIyKiGkWqBkjVQJYrFYqkxaUFCAbt26geM4eHt7w8XFBQMGDMDWrVtL3GZUVBRcXV2xYMECLFiwoAKjJ8URi8XFllGTkpIAAIaGhjJlVBcXFyqjEkKqHErWSJXz/v17HDhwAEKhELdv34aOjg4KCwtx9OhR9OnTp0SzSRlj6NGjB2JiYhAeHk4JgBJ5/fq1TAIXFRUFxhg0NDTg5OQklcC5u7tX+v6thBAiT5SskSrtzp076NChA3R0dJCZmQknJydwHIcxY8ZIlUn/69ixYxg4cCCOHz+Ofv36VWLEpCyys7P5MmpQUBBCQkLw+PFjvozaoEGDYsuolbFbBiGElBcla6RKmzp1Kvz9/RETE4OgoCAIhUIcPXoUBQUF6NGjBziOQ9++faVGznJzc+Hs7AxHR0ecOXOGOnQVJRaLERsby++JWpTIvXnzBgBgZGQkk8A5OztDW1tbwZETQog0StZIlZWQkAB7e3ssWrQIc+fO5R9PT0/ny6R37tyBiYkJP5u0adOmWLp0KZYtW4awsDDY29sr8BWQipCcnCxTRo2OjubLqM7OzjJl1Jo1ayo6bEJINUbJGqmyfHx8cOHCBcTGxkJfX7/Yc6KioiAUCrFz504kJibC3t4e8fHx+Pbbb7Fx48ZKjpgoSnZ2NkJDQ6VG4R4/foy8vDwAgJWVlcwonJWVFY26EkIqBSVrpEoKDQ2Fu7s7Nm3ahO+///6L54vFYly6dAkTJkzAy5cvoa6uLlUmpdJY9SMSiRATEyM1AhcUFIS3b98CAGrUqFFsGVVLS0vBkRNCqhpK1kiV1K9fP4SHhyMiIqLE20NdvHgR3bp1w5YtWyAWiyEUCnH37l2YmJhg1KhR8PX1hZeXF42mVGOMMakyatFIXGxsLBhj0NTUhLOzs9TWWu7u7jA2NlZ06IQQFUbJGqlybt26hbZt22LPnj0YOXJkia4pKCiAu7s76tSpg6tXr/IJWUREBPz9/bFz504kJSWhcePG4DgOo0ePhrm5eUW+DKJCMjMzERoaKjUKFxoaypdRGzZsKDMKZ2lpSYk/IaREKFkjVQpjDO3bt0dWVhYePnxY4hXuV69ejdmzZyMoKAhubm4yx0UiES5dugShUIhjx45BJBKhZ8+efJmUSl/kv0QiEaKjo2XKqCkpKQAAY2NjPnErGolzcnIq8UgwIaT6oGSNVCmnT59Gnz59cPbsWfTo0aNE1yQlJcHe3h4cx2HDhg1fPD8tLQ379u2DUCjEvXv3UKtWLYwaNQocx8HT05NGS8gnMcaQmJgoMxs1NjYWAKClpQUXFxeZ2ag1atRQcOSEEEWiZI1UGRKJBB4eHqhZsyauXLlS4qRp7NixOHfuHKKjo2FiYlKq5wwPD+fLpMnJyXB1deXLpGZmZmV5GaQayszMxOPHj2XKqPn5+QCARo0aSSVwnp6eqF+/Pn0xIKSaoGSNVBm7d+/GmDFjcOfOHbRs2bJE1xTd37Z161ZMmDChzM8tEolw4cIFCIVCHD9+HBKJBL169QLHcejduzeVSUmpiUQiREVFSS0nEhwcjHfv3gEAatasKXMfnKOjI5VRCamCKFkjVUJBQQEcHR3h7u6Oo0ePlugasViMpk2bQlNTE4GBgSW+v+1LUlNT+TLp/fv3UatWLYwePZovkxJSVowxvHr1SqaMGhcXB+BDGbVx48YyZVQjIyMFR04IKQ9K1kiVsHHjRkydOhWhoaFwdnYu0TWbN2/GxIkTERgYiBYtWlRIXE+ePIFQKMSuXbvw+vVruLu7g+M4jBo1CnXq1KmQ5yTVT0ZGBl9GLRqJCwsLQ0FBAQDAxsZGZhSuXr16VEYlREVQskZUXlZWFmxsbNC7d2/s2LGjRNe8e/cO9vb2GDBgALZv317BEX4oaZ0/fx5CoRAnTpyARCJB7969wXEcevXqRWVSIneFhYWIjIyUmY2alpYGAKhVq1axZVQNDQ0FR04I+S9K1ojKW7ZsGZYuXYqYmBhYWlqW6JrvvvsO+/btQ3R0dKWPcL179w579+6FUCjEw4cPYWpqitGjR8PX1xfu7u6VGgupXhhjePnypUwC9/TpUwCAtrY2GjduLLWor5ubGwwNDRUcOSHVGyVrRKWlpKTA2toaEyZMwNq1a0t0zaNHj9C0aVOsX78eU6ZMqeAIPy80NBT+/v7YtWsX3rx5Aw8PD75MWrt2bYXGRqqP9PR0mdmoYWFhKCwsBADY2trKjMLVrVuXyqiEVBJK1ohKmzFjBrZu3Yr4+HiYmpp+8XyJRIK2bdsiMzMTQUFBSlPyKSwsxPnz5+Hn54eTJ0+CMYY+ffrwZVKa4UcqW0FBgVQZteheuPT0dACAqampzKK+9vb2SvM7RUhVQskaUVnPnz+Hvb095s2bhwULFpTomp07d8LHxwdXrlxBx44dKzbAMkpJSeHLpI8ePULt2rUxZswYcBxX7O4KhFQWxhieP38uMxs1ISEBAKCjowNXV1epETg3NzcYGBgoNnBCVBwla0RljR8/HqdOnUJ8fHyJOoOMjAzY29ujY8eO2LdvXyVEWH6PHz+Gv78/AgIC8ObNG3h6evJl0pKMJBJSGdLS0mTKqE+ePEFhYSEEAgFfRv34Xjhzc3MqoxJSQpSsEZUUHh4OV1dXrF+/HpMnTy7RNTNmzMDff/+NqKgo1K9fv4IjlK/CwkKcPXsWQqEQJ0+ehEAgQJ8+feDr64sePXpQmZQonYKCAoSHh8uMwr1//x4AUKdOHZn74Ozt7aGurq7gyAlRPpSsEZU0aNAgBAUFISoqqkTLXoSHh8Pd3R1LlizBnDlzKiHCivP27Vu+TBoUFIQ6derwZVJXV1dFh0fIJzHG8OzZM5kE7tmzZwAAXV3dYsuo+vr6Co6cEMWiZI2onMDAQLRq1Qq7du3CmDFjvng+Ywxdu3bFs2fPEBYWBm1t7UqIsnKEhIRAKBQiICAAKSkp8PLy4suktWrVUnR4hJRIamoqQkJCpBK48PBwiEQiCAQC2Nvby4zCmZubKzpsQioPU3I+Pj6sf//+Uo8dPHiQaWtrs1WrVjHGGHv37h2bOnUqs7KyYpqamszc3JxxHMeePXvGX9OnTx/WuXPnYp/j9u3bDAB7+PDhZ2N5+vQpA8D/GBgYMGdnZzZx4kQWHR1dvhdKSkQikbAOHTowV1dXJhKJSnTNoUOHGAB2+vTpCo5OcfLz89mxY8fYgAEDmIaGBtPU1GSDBw9mJ0+eZIWFhYoOj5BSy8vLY48ePWI7duxgU6ZMYe3bt2dGRkb8318zMzPWvXt3Nnv2bLZ3714WERFR4r8JhKgalUvWtm7dyrS0tNjWrVsZYx8SNTs7O+bi4sJOnz7Nnj17xq5du8batWvH6tSpw+Li4hhjjB09epQJBAKWkJAg8xwTJkxgHh4eX4ylKFm7dOkSS0pKYnFxcezYsWPM29ub6erqskuXLsnnRZNPOnfuHAPATp48WaLzs7OzmaWlJevTp08FR6Y83rx5w9atW8fc3Nz4Tm3GjBksNDRU0aERUi4SiYTFx8ezI0eOsAULFrC+ffuyBg0a8Amcnp4ea9myJfvuu+/Y33//zQIDA1l2draiwyak3FQqWVu1ahXT1tZmhw4d4o9/9913TF9fnyUlJUldl5OTw+rVq8d69OjBGGOssLCQmZmZsbkLFrK0PBF7lytiaXki9iY9kxkaGrINGzZ8MZaiZC0oKEjqcbFYzDp27MisrKzom10FEovFzNPTk7Vt25ZJJJISXbNgwQKmpaXFYmJiKjg65RQUFMSmTp3KTE1NGQDWtGlTtnHjRvbu3TtFh0aI3KSkpLDLly+zNWvWsLFjxzJXV1emrq7OADA1NTXm6OjIRowYwVauXMnOnTvHkpOTFR0yKSWJRMKyCsRS/XdWgbjEfYGqU/p71jiOQ3p6OhwdHbFp0yYcPXoUXbp0AfBhgdNatWph2LBh+Oeff2SuXbxsORb/sgBHHz9DtrYhti5fgJCLJzDz5AN+yvjDk/twbPlP+OtWJGwtasNcTwNWhprQ01CTaS8hIQGNGjVCUFAQPDw8pI4dO3YMAwcOxN27d9G8eXP5vxEE+/btw8iRI3Hjxg20bdv2i+fHx8fD2dkZP/30E5YtW1YJESqvgoICnDlzBn5+fjh9+jTU1dXRr18/cByH7t2700KmpMrJy8srdjZqZmYmAMDc3FxmORFbW1uoqcn+7SeVL0ckwbPMQiTniJCU8+F/CySy52mpAeZ6GrDQ0/xs/63qVOIv9NmzZ3H8+HFcvnwZnTp14h9/+/Yt0tPT4eTkxD/GGENijgiP3uYiTrc+GGO4FBSBeo294NlvFK76b0T8g1uwafahs394fA9cOvXGO3UDpL3JhQSAGgAnEy141dZFXT2NEq0F5OjoCOBDQkfJmvwVFhZi/vz56NOnT4kSNQCYNm0a6tSpo/KzP+VBS0sLAwYMwIABA/D69Wvs2bMHfn5+6NOnD8zNzTF27Fj4+PjAxcVF0aESIhc6Ojrw8vKCl5cX/5hEIsHTp0+lkrddu3bh119/BQDo6+vDzc1NaiJD48aNoaenp6iXUa183H9HpBXw/XExORqvQAI8zxLhZZaozP23KlCJZM3NzQ0pKSn45Zdf0KxZs09uKhydno/rSTlIyRNDAEDyv0FDwf/WoKrTyA5W7s3w4Pge2DRri3cvniIhKBDj/zoI4P//QUgAhKcV4ElaAWrrqKN9XT3Y1fj8DMKiAcqq8g9D2Wzfvh3x8fE4evRoic4/e/YsTpw4gQMHDtC0//8wMzPDtGnT8OOPPyI4OBhCoRA7duzA77//jmbNmoHjOIwYMQI1a9ZUdKiEyJWamhpsbGxgY2ODwYMH84+npKRIzUa9ceMGtmzZArFYDDU1NTg4OEhtreXu7o46deoo8JVUPf/tv4tKfp9L1D5Wnv5bFahEslavXj0cPnwY3t7e6NGjB86dOwdDQ0PUrl0bxsbGeBz2BMcTMhCRVoCiVIkBeJsQAzUNDdSsa8W31bT/aJz4bQ7yslbh4Ym9MLZoAJvm7WWes+gfSkqeGIfjM+Fkkg8H0af/2URERAAAGjVqJKdXTYpkZ2dj8eLFGDNmTInWEcvPz8fUqVPh7e2NIUOGVEKEqkkgEMDT0xOenp747bffcPr0aQiFQkyZMgXTpk3DgAEDwHEcunbtSmVSUqWZmpqic+fO6Ny5M/9YXl4ewsLCpEbhTp48iaysLABA3bp1ZZYTsbGxoTJqKeWKJLjwMkum/y6P4vrvbvUNoKvC5VGV+QtsaWmJa9euwdvbG926dcP58+dhZGSE7v0HY/eevag7/EcYmJrxH1JhXi4CD/rBxbsXdAyN+HZcu/XHydXzEHLuMB6d2o9mA8d+djSsqL3ItAI8TH5f7DkSiQR//vknGjVqBE9PTzm9YlLkzz//xLt377B48eISnb9+/Xp+FI5GOktGW1sbgwYNwqBBg/D69Wvs3r0bfn5+6NWrFywsLDB27FhwHCd1ywEhVZmOjg6aNm2Kpk2b8o9JJBLEx8dLJXD+/v5YsWIFgA9lVHd3d5kyqq6urqJehlKLTs/H2edZyBN/6GnlfQP9x/13QkYaeloawN5YNUfZVGaCwbFjxwAAr169gre3N2rWrIklO4/ixssM/OXTA5o6uug59ReY2Tgh7dUzXPjrV6QlvcBE/3Mwqi29eOLhJT/iyb+nkZeVgVknH8LYomRbD6UlPsdvfZrgzwOnMKStF3JzcxEWFob169cjMDAQp0+fhre3t7zfgmotNTUV1tbW8PHxwR9//PHF81+9egUHBwd8/fXXWLduXSVEWHUxxvDo0SMIhULs2bMHqampaN68OXx9fTF8+HCYmJgoOkRClMKbN29kFvWNjIyERCKBuro6HB0dZUbhqvPevowx3Hmdi+tJOZX+3B0s9NDSTFflvsirXLIGAImJiWjZviOYjgHG/3UQEpEIl7euRvjVs8h8mwyJWAwr92YYt3439GrIdijPQu7jb99esGvZkb9frSSKkrUienp6sLKygre3N6ZNmwZbW9tyvVYia/bs2di0aRPi4+NLdI/IqFGjcPnyZURHR6NGjRqVEGH1kJ+fj1OnTkEoFOLs2bPQ0NCQKpPSfo6ESCv6Mv9xAhcSEoLs7GwAH27v+Th58/T0RKNGjap8GZUxhmtJOQh8nauwGFqZ6aK9hZ5KJWxKn6wV53Zyzmcz8vvHAnD819kYuXIrXLx7VVgcHSz00MqcZglVlJcvX8LOzg6zZs0qUQn02rVr6NixI/z8/MBxXMUHWE0lJSXxZdLw8HDUrVuXL5MWzYomhMiSSCSIi4tDUFCQVBKXlJQEADA0NJQpo7q4uEBHR0fBkcvPl/rvyqJq/f0PQ2IAALBHSURBVLfKJWvR6fk48jTzi+c9+fc03ibEoM2ob6GpU3H3CwxqZKiyNXBl98033+Do0aOIi4uDkZHRZ88ViUTw8vKCvr4+bt26VeW/nSoDxhgePnzIl0nT0tLQsmVLcByH4cOHw9jYWNEhEqISXr9+LVNGjYqK4suoTk5OMmVUVdz7t6T9d2VRpf5bpZK1XJEEW8LTkCuumJCPLv8JwWeKL4t69BqKgfNWyzyuqy7AN84mKj3LRBlFRUXBxcUFq1evxo8//vjF8zdu3IgpU6bg3r17UjcEk8qRn5+PEydOQCgU4ty5c9DU1MTAgQPBcRy6dOlCZVJCSiknJ4cvoxaNxD1+/Bg5OR9GpRo0aCCTwDVq1EhpS3sV3X+XhSr13yqVrB1PyEBkWoHcZ4wUyUp9i7ys4rN+HQNDGNSsLfO4AB8W4OvX8PMjP6R0hg4dinv37iEqKuqLJYC3b9/C3t4eQ4cOxZYtWyopQvIpSUlJCAgIgJ+fHyIiIlCvXj2MGzcOPj4+cHBwUHR4hKgssViM2NhYmV0ZkpOTAQBGRkbFllG1tRU/elTR/XdZqFL/rTLJmrINn/7XYGvDKrHwnjK4f/8+mjdvXuJ7z77++mscPnwY0dHR1XqGlbJhjOHBgwfw8/PD3r17kZ6ejlatWvFlUpoAQoh8JCcnS5VRg4KCEB0dDcYYNDQ0+DJq0dZa7u7ulbroNfXf5acSyRpjDNsj0/EuT6xUWXkRAQBTHXWMdzRW2iFoVdK1a1ckJibi8ePHXyyf3b9/Hy1atMCGDRvwww8/VFKEpLTy8vL4Mun58+ehpaWFQYMGgeM4dOrUicqkhMhZdnY2QkNDpUbgHj9+jNzcD7MwLS0tZcqoDRs2LHUf9vbtW+jq6sLAwKDY49R/y4dKJGuvsguxK7r4BWmVyVj7Gqinr6noMFTapUuX0LVrVxw7dgz9+/f/7LkSiQStWrVCfn4+Hjx4QKvsq4jExES+TBoZGYn69evzZVJ7e3tFh0dIlSUWixETEyOVwAUFBeHNmzcAgBo1akiVUT09PeHs7AwtLa1Ptunq6orU1FQcOXIELVq0kDlO/bd8qESydjIhA+FKVuv+LwEAZxMt9FWB2reyYoyhWbNm0NTUxO3bt7/4LWfHjh346quvcP36dbRr166SoiTywhjDvXv3IBQKsXfvXrx//x5t2rQBx3EYOnQolUkJqSRJSUky98HFxMSAMQZNTU04OztLjcC5u7vDxMQE2dnZ/F7d6urqWLNmDSZPniz1t5v6b/lQ+mQtRyTBxtDUEm/mqkhqACa51oSeCswsUUaHDh3C0KFDcfXqVXTo0OGz56anp8Pe3h5du3bF7t27KylCUlHy8vJw/PhxCIVCXLhwgd/+qqhMSkuxEFK5srKyii2j5uXlAQCsrKxgaWmJGzduSF03aNAg+Pn5wcjIiPpvOVL6ZC0iLR/HE5T3xsT/GtDQEI4myn2jojISiURwcXGBjY0Nzpw588Xzp06dih07diAqKgp169athAhJZXn16hV27doFoVCIqKgoNGjQAOPGjQPHcbRLCCEKJBKJEBMTwy8lcvLkSURGRsqcZ2RkhDNnzqCmc1Pqv+VEOVPIjyTniJQ/yP9Rw4d4Sen5+fkhOjqa3xD5c8LCwrBp0yYsWLCAErUqqF69evj5558RERGBO3fuoFevXti4cSPs7OzQrl07bN++HRkZGYoOk5Bqp2hm6ahRo/Dbb7+hQ4cOUvcKF42AZ2Rk4MKFC9R/y5HSj6ztiUnH8yzlfQP/y9JAE6Ps6F6b0sjNzYWtrS06dOiAPXv2fPZcxhg6deqExMREhIaGfvbGV1J15Obm4tixYxAKhbh48SJ0dHQwePBgcBwHb29vKpMSogAtW7bE3bt3AQAWFhbo27cvevXqhU6dOsHQ0JD6bzlS6ulzjDGlznSLk5wjAmNMqacAK5uNGzfizZs3WLp06RfPPXDgAK5evYpz585RolaN6OrqYuTIkRg5ciRevnzJl0kDAgJgaWkJHx8f+Pj4wMbGRtGhElJtTJs2DS9fvkTPnj3h5OQk1e9R/y1fSj2yll0owYawVEWHUWqTG9eEviZ90y+J9PR0WFtbY+TIkdi0adNnz83KyoKTkxOaNm2Ko0ePVlKERFkxxnDnzh0IhULs378fGRkZaNeuHT+btGiWGiGk8lH/LV/KF9FHCiVKm0d+lkh581+l89tvvyE/Px8LFiz44rkrVqxASkoK1q5dWwmREWUnEAjQunVrbNmyBUlJSdi9ezd0dHQwYcIEmJubY9y4cbhy5QokElWYi0ZI1UL9t3wpdbKmop81xNQ3lEhSUhLWr1+PadOmwdzc/LPnxsTEYM2aNZg9ezYaNWpUSRESVaGnp4dRo0bhwoULePbsGebNm4c7d+6gU6dOsLa2xsKFCxEfH6/oMAmpNqj/li+lTtbUlK9sXCLqSv2uKo+lS5dCV1cXM2fO/OK5P/74IywsLDB79uxKiIyosgYNGmDu3LmIjo7GzZs30bVrV6xbtw42Njbo0KEDhEIhsrKyFB0mIVUa9d/ypaRhfaCpop+2hhLenKhsYmNjsXXrVsyZM+eLK9WfOnUKZ86cwdq1a6Grq1tJERJVJxAI0KZNG2zduhXJyckICAiApqYmxo8fD3Nzc3Ach6tXr1KZlJAKQP23fCn1BAPGGNY9focCFfpbqqUmwDS3mko5m0SZjBw5Ejdv3kR0dPRnE7C8vDx+sdzz58/T+0rK7dmzZ/xs0ri4ODRs2JCfTUoldkLkg/pv+VLqkTWBQABzPaVeXUSGuZ6GUn7QyiQoKAj79u3DokWLvjhStmbNGjx//hx//vknva9ELqysrDB//nzExMTgxo0b6Ny5M9asWQNra2t4e3vD39+fyqSElBP13/Kl1MkaAFjoaSp/kP+jBqCuiv3jVIQ5c+bAwcEBPj4+nz3v+fPnWL58OX788Uc4OjpWUnSkuhAIBGjbti22bduG5ORk7Ny5E2pqauA4Dubm5vD19cW1a9eoTEpIGVH/LT9K/z6a62moxCawACABVO6bRGW7cuUKzp8/j+XLl0ttU1Kcn376CTVq1CjRsh6ElIe+vj7Gjh2Ly5cvIyEhAbNnz8b169fRsWNH2NnZYcmSJUhISFB0mISoFOq/5Uep71kDgByRBBtDU1XiAxeLCnFp/niMHjoYw4YNg5GRkaJDUiqMMbRq1QoSiQR379797HDzv//+i86dO2PXrl0YM2ZMJUZJyAeMMdy8eRN+fn44cOAAsrOz4e3tDY7jMHjwYOjr6ys6REKUmir132oAJrnWhJ6Gco5hKWdUH9HTUIOTiRaUs4r8/wQAjLJToC1g+Oabb2Bubs5/U6cyygfHjx/H3bt3sXLlys8maoWFhZg8eTLatGmD0aNHV2KEhPw/gUCAdu3aYceOHUhOToa/vz8AwMfHB+bm5vjqq69w48YNKPn3XUIURpX6bycTLaVN1AAVGFkDgFfZhdgV/V7RYXzRWPsaqKeviZcvXyIgIAB+fn6Ijo5GgwYN+Nlmtra2ig5TIUQiEdzc3FC/fn1cuHDhs+euX78eM2bMwMOHD+Hh4VE5ARJSQgkJCdi5cyeEQiGePn0Ka2trcByHcePGwcrKStHhEaJUVK3/VlYqkawxxrAjMh0peWIoY7ACAKY66hjvaCyzkW1gYCCEQiH27duHjIwMtG3bFhzHYdiwYdVq70I/Pz+MHz8eDx48QJMmTT553uvXr2Fvb4/Ro0fjr7/+qsQICSkdiUSCGzduQCgU4uDBg8jOzkanTp3AcRwGDRpEZVJCoLr9t7JRiWQNAGLe5+NwfKaiw/ikwdaGsKuh/cnjubm5OHbsGIRCIS5evAhdXV0MHjwYHMehY8eOUFNT3uHX8srLy4O9vT1atWqF/fv3f/ZcX19fnDhxAtHR0ahVq1YlRUhI+WRlZeHw4cMQCoW4evUqDA0NMWzYMHAchzZt2ih1J0BIRVP1/lsZqEyyBgDHEzIQmVagVNl5Ua27X8OSTyZ48eIFvyhnTEwMLC0t+TKpjY1NxQWrIGvXrsWsWbMQHh4Oe3v7T54XGBiIVq1a4e+//8a3335biRESIj/x8fHYuXMn/P39kZCQAFtbW/j4+GDcuHGwtLRUdHiEKERV6b8VRaWGc7rVN4COunJ9Q9VRF6BrfYNSXVO0d2FUVBRu3bqF7t27Y/369bC1tUWHDh3g5+eHzEzl/RZSGu/fv8eKFSvw1VdffTZRE4vFmDRpEry8vDBhwoRKjJAQ+bK2tsaiRYsQFxeHK1euoE2bNvj111/RsGFDdO3aFbt370ZOTo6iwySk3DiOg0AggEAggKamJqytrfHTTz8hOzsbCQkJ/DGBQIABjWrgZ6/a2D/ve/76wrxcLO5giyUd7VCYlyvVdvyDW5jjVRu5mdL3u235uj9O/j5PLvF/3H/n5uZi4cKFcHBwgLa2NkxNTTFkyBA8efJELs9VXiqVrOlqqKGnZekSo4rW09IAumWcQSIQCNC6dWts2bKF37tQS0sLX331FczNzeHj44MrV66o9GzSNWvWIDs7GwsXLvzseTt27MDDhw+xYcMGqKurV1J0hFQcNTU1dOzYEUKhEMnJydi+fTsKCgowZswYmJub4+uvv8atW7doNilRaT169EBSUhLi4+OxbNky/PXXX/jpp5/445cuXUJSUhKSkpJwMzIB/X9exR8Lu3wK5jaOqGNtj7B/T1d67EX9d35+Prp06YIdO3Zg6dKliI6OxpkzZyAWi9GiRQsEBgZWemz/pVJl0CK3k3NwPUnx30w7WOihlbme3Nt9/vw5XyaNjY3l9y4cN24crK2t5f58FeX169ewsbHBpEmTsHLlyk+el5qaCnt7e/Tu3ZtfHoGQqiouLo4vkz579gx2dnbgOA5jx45FgwYNFB0eISXGcRzS09Nx7Ngx/rGvv/4ap06dwp07d9CoUSMEBQVJzer/uP/e+s0AuHUbCIDh8YXj+HrLUQBAWuJz/NZHeiKaV9/hAIBHJ6Xve5516iHSEl9g6zcD8NXmwzj35xK8eRoNC/vGGLLoT9RuWPwKDB/336tWrcKcOXMQFBQEd3d3/hyJRIIWLVogJycHYWFhCr33VKVG1oq0MtNFK7PP7ylZGTG0rKAYLC0tMW/ePERHR+PmzZvo3Lkz1q5dCxsbG/6buirsXbhs2TJoampi9uzZnz3vl19+QUFBAVatWvXZ8wipCmxsbLB48WLEx8fj33//RcuWLbFs2TJYWVmhW7du2LNnD5VJicrS1dVFYWHhJ48X9d/vXjzF88cP4NatP1y79sfzx/eR+jIBAFDDrB5G/+4HAJh+NBBzL4Sh708r0PenFbB0a4ZmA8di7oUwzL0Qhhpm9fi2L2xagV7TF+OHgItQU1fH4cVTPhnDx/33nj170LVrV6lEDfgwOj5t2jSEh4cjJCSkrG+JXKhksiYQCNDeQg8dLOQ/qlUSHSz00KGufoVn2QKBAG3atMG2bduQlJSEXbt2QUNDA+PHj4e5uTk4jlPavQvj4+Pxzz//4Oeff4aJicknzwsJCcHmzZuxaNEimJubV2KEhCiWmpoavL29sXPnTiQnJ2Pbtm3Iy8vD6NGjYWFhgW+++QZ37tyhMilRGffu3cOePXvQuXNn/rHWrVvDwMCA/wkODkZ7Cz28uXwQ9m06Q9fIGHo1TGDfuhMeHN8DAFBTV4dejQ/9hkFNUxiamkHH0Ag6hkZQ19SEpo4uDE3NYGhqBrWPbpvp9sNcWDdpAzNrB3TwnYJnIfdRmJ8nFWNx/Xd0dDScnJyKfU1Fj0dHR8vnTSojlUzWgA+JTCtzPQxqZAhddUGFr5AsAKCrLsCgRoYVUvr8En19fYwZMwaXLl3C06dP8fPPP+PmzZvo2LEjbG1tsXjxYjx9+rTS4/qUhQsXwtTUFJMnT/7kOYwxTJo0CQ4ODp89j5CqzsjICOPHj8f169cRExODqVOn4vz582jdujUcHR3x66+/4uXLl4oOkxAZp06dgoGBAXR0dNCqVSu0b98eGzZs4I/v378fwcHB/I+zszMkEgkuH96D77ixfP/t0WsIHp3aD4lYXOZYzO2d+f9vZGoGAMhOTSlX/130ZUlLS6vMccmDyiZrReyNtfGNswkcTT68kfJO2oraczLRwjfOJrA3VvxaLFZWVpg/fz5iYmJw/fp1eHt7Y/Xq1bC2toa3tzf8/f2RnZ2tsPgeP36M3bt3Y+HChdDT+/Qvxp49e3Dz5k38+eef0NRU3pWjCalMtra2WLJkCZ4+fYpLly6hefPmWLp0KSwtLdG9e3fs3bsXubm5X26IkErg7e2N4OBgREVFIS8vD0eOHEGdOnX44w0aNICtrS3/o62tjfPnz+PVq1eYNn4MZniZYW4zc+yb8w3ev05ETOCVMseirvFRP/K/kTPGJF/sv+3s7BAeHl7sscjISAD47GoGlUHlkzXgwyzR/g2NMKiRIUx1PgyJljdpK7reVEcdg60N0a+hUZlnfVaUor0Lt2/fjuTkZOzcuRNqamrgOA7m5ub8N/XKLqPMnTsXtra2GD9+/CfPyczMxMyZMzF48GB06dKlEqMjRDWoqamhc+fO2LVrF5KTk7F161bk5ORg1KhRsLCwwHfffYfAwEAqkxKF0tfXh62tLaysrEr8pXv79u0YMWIEP9oWEhyM49fvoUWfoXhwbDcEANT/19Z/R9rUNbXAJJ8fffu4/+/RQP+L/ffIkSNx6dIlmfvSJBIJ1q1bh6ZNm8LZ2fkTV1cO5co+ysneWBvjHY0x1r4GnE20+BdX0hfJny8AXEy0Mc6+BsY7Giv9ysbAh1+Yoo3jnz59ipkzZ+Lq1avo0KED/009ISGhwuO4ceMGTp8+zU8u+JRly5YhPT0da9asqfCYCFF1RkZG/Mbx0dHRmDRpEk6fPo1WrVrByckJK1euxKtXrxQdJiFf9PbtW5w8eRI+Pj5o3Lgx/9OntReWTJ6AqOvnUU+SgZoWDSAQCBB94wKy0lKQn/NhUp2JRQO8CHuEtMTnyE57J3PPdlH/3cfqwzJfjYy+3H9PmzYNzZs3R9++fXHw4EE8f/4c9+/fx+DBgxETEwOhUCj396HUWBWWXShmEal57N+XWWx3dDpbE/yW/fpI9mdN8Fu2OzqdXXmZxSJS81h2oVjRocuFWCxm165dY76+vkxfX58BYJ06dWI7d+5kWVlZcn8+iUTCWrduzby8vJhY/On3MDIykmlqarIlS5bIPQZCqguRSMQuXrzIRo8ezXR0dJiamhrr0aMH27dvH8vNzVV0eKQa8PHxYf379y/22NOnTxkAFhQUJPX46tWrmbGxMSsoKJC5prCwkNWsWZOtWbOGZReK2eQ5v7CadcyYQCBgTfsOZ78+esumHw1kDVybMk0dXQaAzTv9kM3bdZIBYHefJvP9d1BQEAPAnj59WqLXkpWVxebNm8dsbGyYhoYGA8BsbW3ZixcvSvOWVBiVXGetrBhjyBExiBiDWAKoqwEaAgH0NARVfu++/+5daGBgwO9d2LZtW7m8/pMnT6Jfv344f/48unXrVuw5jDH06NEDsbGxePLkCXR0dMr9vIRUd+/fv8eBAwcgFApx+/ZtGBsbY8SIEeA4Ds2bN6/yf99I1VfZ/ffZs2cxcOBArF69GpMmTZJ7+6VVrZI18sHTp0+xc+dOCIVCJCQkwMbGBhzHlWvvQrFYDA8PD9SpUweXLl365C/PsWPHMHDgQBw/fhz9+vUrz8sghBQjOjoa/v7+2LlzJ16+fAknJydwHIcxY8agbt26ig6PEJVx5coV3LhxAxMnToSpqalCY6FkrRqTSCS4ceMGhEIhDh48iJycHHTq1Akcx2HQoEGfncn5X7t27cK4ceNw9+5dNG/evNhzcnNz4ezsDCcnJ5w+fZq+7RNSgcRiMf79918IhUIcOXIEBQUF6N69OziOQ79+/WhUm1QbLi4uePbsWbHH/vnnH4wePbqSIyo9StYIgA9l0kOHDkEoFOLatWswNDTEsGHD4Ovri9atW382scrPz4eDgwOaNGmCw4cPf/K8xYsXY/ny5QgLC1P4NGhCqpP09HS+THrnzh0YGxtj5MiR8PX1RdOmTemLE6nSnj179sldFczMzGBoaFjJEZUeJWtERnx8PF8mffbsGWxtbfm9C4srk/7555+YNm0anjx5AkdHx2LbTEhIgJOTE6ZOnfrZfUIJIRUrKiqKL5O+evUKzs7OfJnUwsJC0eERQopByRr5JIlEgmvXrkEoFOLQoUPIzc1F586dwXEcBg4cCD09PWRmZsLGxgb9+vXDtm3bPtnW4MGDERgYiKioKBgYGFTiqyCEFEcsFuPSpUsQCoU4evQoCgsL0aNHD75Mqq2t/EsWEVJdULJGSiQzM5Mvk16/fh1GRkYYPnw4xGIxAgICEBsbiwYNGhR77cWLF/kNqkeOHFnJkRNCviQ9PR379++HUChEYGAgTExMMGrUKHAchyZNmlCZlBAFo2SNlFpcXBz8/f3h5+eHly9fwsTEBD/99BPGjh0rk7AVFBTA3d0dderUwdWrV+mPPiFKLjIyEkKhEDt37kRSUhJcXFz4Mqm5ubmiwyOkWqJkjZTZjz/+iK1bt6JPnz44efIk8vLy0KVLF/j6+mLAgAHQ1dXF6tWrMXv2bAQFBcHNzU3RIRNCSkgkEvFl0mPHjkEkEqFHjx7w9fVFnz59qExKSCWiZI2UybNnz2Bvb49ffvkF8+bNQ0ZGBg4dOgQ/Pz/cvHkTRkZG6NevHw4fPoyvvvoKGzZsUHTIhJAySktLw/79++Hn54d79+6hZs2afJnUy8uLRswJqWCUrJEy4TgO586dQ1xcHPT19aWOxcbGwt/fH2vXrkVOTg6/qfu4ceNQr149BUVMCJGH8PBw+Pv7Y9euXUhKSkLjxo35MqmZmZmiwyOkSqJkjZRaWFgY3NzcsHHjRkycOLHYc27evIl27dphxowZSE5OxpEjR5Cfn4+uXbuC4zj0798furq6lRw5IUReRCIRLl68CD8/Pxw/fhxisRi9evUCx3Ho06cPtLS0FB0iIVUGJWuk1AYMGIDQ0FBEREQU+wdZLBajadOm0NTURGBgINTU1PD+/XscPHgQQqEQt27dQo0aNfi9C1u0aEFlFEJUWGpqKvbt2wehUIj79++jVq1aGDVqFHx9feHh4UG/34SUEyVrpFRu376NNm3aYPfu3Rg1alSx52zevBkTJ05EYGAgWrRoIXM8JiYG/v7+8Pf3x8uXL+Ho6MiXUahMSohqe/LkCV8mTU5OhpubGziOw+jRo1GnTh1Fh0eISqJkjZQYYwwdOnRARkYGHj16BDU1NZlz3r17B3t7ewwYMADbt2//bHvF7V3YrVs3vkxKexcSorpEIhHOnz8PoVCIEydOQCKR8GXS3r17U5mUkFKgZI2U2NmzZ9GrVy+cOXMGPXv2LPac7777Dvv27UN0dHSpvkW/f/+e37vw9u3b/N6FHMehWbNmVEYhRIW9e/eOL5M+ePAApqamGD16NDiOg4eHh6LDI0TpUbJGSkQikcDLyws1atT45OK2jx49QtOmTbF+/XpMmTKlzM/1370LnZyc+DJp3bp1y/MyCCEKFhYWBqFQiICAALx+/Rru7u58mbR27dqKDo8QpUTJGimRPXv2YPTo0bh9+zZatWolc1wikaBt27bIzMxEUFAQNDQ0yv2cYrEYly9f5vcuLCgo4Pcu7Nu3L5VJCVFhhYWFUmVSxhj69OkDjuPQq1cvaGpqKjpEQpQGJWvkiwoKCuDk5ARXV1ccO3as2HN27twJHx8fXLlyBR07dpR7DOnp6XyZ9M6dOzAxMeHLpE2bNqUyKSEq7N27d9i7dy+EQiEePnyI2rVr82VSd3d3RYdHiMJRska+6K+//sKkSZMQGhoKFxcXmePv37+Hg4MDOnbsiH379lV4PJGRkXyZNDExEc7OznyZ1MLCosKfnxBScR4/fgx/f38EBATgzZs38PDw4Mukpqamig6PEIWgZI18VlZWFmxtbdGjRw8IhcJiz5kxYwb+/vtvREVFoX79+pUWm1gs5vcuPHr0KL93YVGZlPYuJER1FRYW4ty5cxAKhTh58iQA8GXSnj17UpmUVCuUrJHPWr58OZYsWYLo6GhYWVnJHA8PD4e7uzuWLFmCOXPmKCDCD4r2LhQKhbh79y5MTEz4RTlp70JCVFtKSgr27NkDoVCIoKAg1K5dG2PGjAHHcXBzc1N0eIRUOErWyCe9e/cO1tbWGD9+PNatWydznDGGrl274tmzZwgLC1OakayIiAi+TPrx3oWjR4+Gubm5osMjhJRDSEgIXyZ9+/YtvLy8wHEcRo4cSWVSUmVRskY+6aeffsI///yD+Pj4YqfUHz58GEOGDMHp06fRq1cvBUT4eUV7FwqFQhw7dgxisRg9e/bky6S0KCchqquwsBBnzpyBUCjEqVOnIBAI0LdvX3Achx49elCZlFQplKyRYr148QJ2dnaYM2cOFi5cKHM8JycHTk5OcHNz4+8nUWapqal8mfTevXv83oUcx8HT05PKpISosLdv3/Jl0uDgYJiZmfFl0saNGys6PELKjZI1UqwJEybgxIkTiIuLg6GhoczxX375BatWrUJ4eDhsbGwUEGHZhYeHQygU8nsXurq68mVSMzMzRYdHCCmH4OBgCIVC7N69GykpKWjSpAlfJq1Vq5aiwyOkTChZIzIiIiLQuHFjrFu3rtidCOLj4+Hs7IyffvoJy5YtU0CE8iESiXDhwgUIhUIcP36c9i4kpAopKCjgy6SnT5+GQCBAv3794Ovri+7du8tl4W5CKgsla0TG4MGD8ejRI0RGRhY7aaB///4ICgpCREQE9PX1FRCh/KWmpvKLcj548AC1atXiF+X09PRUdHiEkHJ48+YN9uzZAz8/Pzx+/BhmZmYYO3YsOI4rdu1IQpQNJWtEyr1799CiRQvs3LkTY8eOlTletJn7gQMHMHToUAVEWPHCwsLg7++PXbt2Se1dOGrUqFJtTk8IUS6MMaky6bt379C0aVO+TFqzZk1Fh0hIsShZIzzGGDp37oy3b98iODgY6urqUsfz8/Ph6uqK+vXr4/Lly1X+pnyRSMTvXXj8+HEwxtC7d29+70IqkxKiugoKCnD69Gm+TKquro7+/fuD4zh069aNyqREqVCyRngXLlxA9+7dceLECfTt21fm+KpVqzBv3jyEhIRUu9LBf/cuNDU1xejRo+Hr60t7FxKi4l6/fo3du3fDz88PYWFhMDc358ukzs7Oig6PEErWyAcSiQTNmjWDrq4ubty4ITNq9urVKzg4OODrr78udoHc6iQ0NJQvk368d+GoUaOKXY+OEKIaGGMICgriy6Spqalo3rw5OI7DiBEjYGJiougQSTVFyRoBABw4cADDhw/HjRs30LZtW5njo0aNwuXLlxEdHY0aNWooIELl89+9Cxlj/N6FvXr1okU5CVFh+fn5OHXqFIRCIc6ePQt1dXUMGDAAHMeha9euVCYllYqSNYLCwkI4OzvDwcEBp06dkjl+7do1dOzYEX5+fuA4rvIDVAEpKSl8mfTRo0e0dyEhVUhycjJfJn3y5Anq1q2LsWPHwsfHB05OTooOj1QDlKwR/PPPP/j+++8RHBwsk1iIRCJ4eXlBX18ft27dgpqamoKiVB2PHz+GUCjk9y709PTky6S0dyEhqosxhkePHkEoFGLPnj1ITU1FixYtwHEchg8fTmVSUmEoWavmcnJyYGtri86dO2PXrl0yxzds2ICpU6fi/v37aNKkiQIiVF2FhYU4e/YsXyYVCATo06cPfH19ae9CQlRcfn4+Tp48yZdJNTU1MWDAAPj6+qJLly4ys+kJKQ9K1qq5VatWYcGCBYiMjIS1tbXUsTdv3sDBwQFDhw7Fli1bFBRh1fDfvQvr1KnDl0ldXV0VHR4hpBySkpL4Mml4eDjq1q2LcePGwcfHB46OjooOj1QBlKxVY2lpabC2tsaYMWOwYcMGmeMTJkzAkSNHEB0dTeU7OQoODoa/vz8CAgKQkpICLy8vvkxKexcSoroYY3jw4AFfJk1PT0fLli35MqmxsbGiQyQqipK1auznn3/Gxo0bERcXJ7OB+f3799GiRQts2LABP/zwg4IirNoKCgpw9uxZ+Pn5Se1dyHEcevToQbPNCFFheXl5fJn03Llz0NLSwsCBA8FxHDp37kxlUlIqlKxVU69evYKtrS1mzpyJJUuWSB2TSCRo1aoV8vPz8eDBA0oaKkFxexcWlUkbN26s6PAIIeWQmJiIgIAACIVCREREoF69enyZ1MHBQdHhERVAyVo19e233+Lw4cOIi4uTWTdtx44d+Oqrr3D9+nW0a9dOQRFWXx/vXZiSkkJ7FxJSRTDGcP/+fQiFQuzduxfp6elo1aoVfH19MWzYMFrDknwSJWvVUHR0NJydnfHbb79h+vTpUsfS09Nhb2+Pbt26ISAgQEEREqD4vQuLyqTdu3enEU9CVFheXh5OnDgBPz8/XLhwAVpaWhg0aBA4jkOnTp2oTEqkULJWDQ0fPhx37txBdHQ0dHR0pI5NnToVO3bsQFRUFOrWraugCMl/vX79mi+ThoaG/h979x0WxfU1cPy7S28CKgoWBAUUe++9d429s2hMMT3RdGNMsSQaTWJiTKKsvSQx9l4jllixAaIoYgErSK877x/+mNcNmqCCu6vn8zz7RGZn75ydhdyz98zcq65dGBQU9Myt0yrE0+bKlStqmTQyMpJy5coxYsQIdDod/v7+pg5PmAFJ1p4xR44coX79+sybN4/g4GCj506ePEmdOnWYPHky48aNM1GE4t8oikJYWBghISHq2oUNGjRQ1y6UMqkQlktRFA4ePKiWSe/cuUOzZs3Q6XQMGDCAYsWKmTpEYSKSrD1jOnbsyOXLlzlx4oRRGU1RFNq0aUN8fDwnTpzA1tbWhFGKgsjMzFTLpBs2bJC1C4V4iqSnp7N69Wr0ej1btmzB3t7eqEwqq8k8W4rs027dujUajYZly5YZbZ85cyY+Pj5FddgCmzRpElZWVkyZMiXfc61bt+bNN9802rZr1y40Gg2JiYlPJsCHcL9472f79u1s3bqVSZMm5evIV6xYwe7du/n222+NEjWdTkfv3r0LOWJRGOzs7OjTpw9r1qzhypUrTJ48mfDwcLp27Yq3tzfvvfceERERpg5TCPEIHBwcGDRoEJs2bSI2NpZPPvmEQ4cO0aFDB3x8fBg/fjznzp0zdZjiCSn0ZC0rK0v9t729PR9//DHZ2dmFeozCaC8kJIR3332XefPmFUJE5k9RFD744AMaNWpEr169jJ5LSUnhnXfeoXfv3nTq1MlEEYrHUbp0ad5++21OnDjB4cOH6du3L7/++itVq1alUaNGzJ49m4SEBFOHKYR4BOXKleP9998nMjKSffv20aVLF7777jv8/f1p0aIFc+fOJTk52dRhiqKkFJCbm5tSvHhxpWfPnsqFCxfU7UFBQUqvXr2USZMmKV5eXkqFChUURVGUVq1aKcHBwUrJkiWVH374Qd1/xowZ6j55fvzxR6VixYqKjY2NEhAQoCxYsMDoeUCZPXu20rNnT8XR0VH55JNPlAkTJii1atVS5s6dq5QvX15xcnJSXnrpJSUnJ0eZOnWqUrp0acXDw0P54osv1HYMBoOSkpWrrNuyXfEqU1a5npyueJUpo+zatcvo/QBGjwsXLuTbFhQUpL7P1157TRk3bpzi7u6ulC5dWpkwYUJBT6ty8eJFpWfPnoqTk5Pi4uKi9O/fX4mPj893fu/1xhtvKK1atfrXeHfu3KkAyrp165SaNWsqNjY2CqDMnTtXbSfvHH7wwQeKvb29cv78eaPPZ8KECfna3rlzZ4HfmzCtjIwM5ffff1e6d++uWFlZKXZ2dsrAgQOVjRs3Kjk5OaYOTwjxGNLS0pQlS5YoHTt2VDQajeLo6KgMHz5c2b59u5Kbm2vq8ApdXv+dkJGj3ErPURIycpSUrFzFYDCYOrQnosAXtfz1119YW1vzxRdf0LlzZ6PrmrZv306xYsXYunUryj2XwBUrVowPP/yQzz77jKCgIJycnPK1++eff/LGG28wc+ZM2rdvz7p16wgODqZcuXK0adNG3W/ChAlMnjyZGTNmYGVlRUhICNHR0WzcuJFNmzYRHR1Nv379uHDhAgEBAezevZude0J5efTzuNVointgHeLTcsgywIpv5+DXrjdzz6bg1643Y6f9yFtlauHlaMOYiV8ReSaKmjWqq5PFenh48Mcff9C3b1/OnDlDsWLFcHBwUGObP38+b7/9Nn///Tf79+9Hp9PRrFkzOnTo8F+JMr1798bJyYndu3eTk5PDmDFjGDhwILt27SrQ5/Ltt98SFRVF9erG8cbExAAwbtw4vvnmG15++WVSU1P5/PPPGT58uLqIeGZmJtOnT+eDDz7A19fXqO2xY8cSERFBUlISISEhAHIBuwWxs7Ojb9++9O3b12jtwi5dulCmTBmGDx+OTqeTtQuFsEAODg4MHjyYwYMHc+nSJRYuXIher2fhwoV4e3sTFBREUFAQlSpVMnWojyQtx8DF5Gzi03KIS8tW++9/stWCp6M1Xo42eDpaU8HFBkfrp+96vgK/oxo1ahAYGEhISAixsbFGyYSTkxO//vor1apVyzfb+pgxY7C3t+ebb765b7vTpk1Dp9MxZswYAgICePvtt+nTpw/Tpk0z2m/IkCGMHDmSihUrUqFCBeDuTPvz5s2jatWq9OjRgzZt2nDmzBnGffk1UXZeJNXrhYePH+u27SA25e4HnZGSzKnt66jTrR8Atbv24/i2dUTFJ3Doejo7bmu5ka0hPseaXJcSlC5dGisrKzVJKVWqFJ6enkaTF9asWZMJEybg7+/PiBEjqF+/Ptu3b//Pc7pt2zZOnDjBkiVLqFevHo0aNWLhwoXs3r2bQ4cO/feHAri6umJra4ujoyOenp54enoazc8zYcIErly5QkxMDCtWrODatWv8+eef6vNXrlzBy8uL9957L1/bzs7OODg4YGdnp7YtNx5YJi8vL8aOHcupU6c4dOgQzz33HD///DOBgYE0adKEOXPmmOX1mEKI/1a+fHk+/PBDzpw5w969e+nUqRMzZ87Ez8+PVq1aERISYhFlUkVRuJKazdqYJGadvM3qmGQOXU9X++/7yTJAbEoOh66nszommVknb7M2JokrqdlGg0eWrsDJmrOzM87OzhQvXpyMjAyio6PV52rUqPHATtzOzo7PPvuMr7/+mps3b+Z7PiIigmbNmhlta9asWb4Lo+vXr5/vtT4+Pri4uKg/O7iXxMXbn8XnkglPyMIAOBf3IPn2/x/3+KY/KF6uAl4Bd5PKMpVrULxcBU5s/pO83wUFuJ2Ry8KoO8yLTOTsncx/OzXUrFnT6GcvLy+uX7/+r6+Bu++9fPnylC9fXt1WtWpV3NzcCu3C8Dp16jBhwgQGDRpE69atqVy5stp2VFQUycnJzJgxw2ikUDy9NBoN9evXZ9asWcTFxbFixQqKFy/OmDFj8PT0ZPDgwWzevJnc3FxThyqEeEgajYamTZvy888/Ex8fz+LFi7Gzs2PUqFF4enoSFBTEzp07MRgekPmYUFRiJnMjE1kYdUftvwEKGum9+4cnZBW4/7YUBS6DhoWFGf3s4eGh/vt+5c17DRs2jGnTpvHFF1/c905QjUZj9LOiKPm23e8YeaW89BwDWy6nEJuaQ6727ltS82mNBuWeX8zDq5dwPTqSjxp4/v/xDAYOrV5Mw74j/n/b//57MyOXP84nkxOf+sD3lxfHve+nIH8M93uf/9yu1WrzfTt4mBss5s+fz7Vr1/j888+N4svIyGDTpk04Ozsb3e1Z2DeDCPNlZ2dH//796d+/P3FxcSxatIiQkBA6d+4saxcKYeEcHR0ZMmQIQ4YMITY2Vi2TLliwgAoVKqhl0ooVK5o0zrz+OyIhi7ze8HHHw/7Zfwe6Z9KxnDMOFlweLXDkfn5+Ro+HWcNMq9UyefJkZs+erV5LlScwMJDQ0FCjbfv27SMwMLBAbUclZvJzeAKRCVn/uW/82XCuhIcx+udVvLZ0p/p44dc1XDl9jPhzd0ecrGxsUQx3RxbyPvSrGXd/jc7cTitQXAVRtWpVYmNjuXTpkrotPDycO3fuqO/fw8ODuLg4o9f9M3G2tbV94EjIt99+y+jRo/Hz8yMhIYGoqCiqVKnC9OnTuXPnDnZ2do/ctnh6eHl5MW7cOE6fPs3Bgwfp2bMns2fPpkqVKuo39Tt37pg6TCHEI/D29uajjz4iKiqK0NBQOnTowIwZM6hUqRKtW7dGr9eTkpLyxOP6Z/9d2EXLvPYiE7L4OTyBqETLHWUrcLK2Z88eLly4wO7du3njjTe4fPnyQx2oW7duNGrUiDlz5hhtHzduHHq9np9++omzZ8/yzTffsHLlSsaOHfuv7SmKQmqOgZUXkknPVQr0IR9atZhy1eriW68pnn6B6sOnTmO8a9bn8KrFALh7lefSqaMkXI0lNeEWBoMBV6/yaDQapi1ayYZTFwul/t++fXtq1qzJ0KFDOXr0KAcPHmTEiBG0atVKLfu2bduWw4cPs2DBAs6ePcuECRM4deqUUTs+Pj78/fffxMTEcPPmTaNRvbS0NFq3bs2pU6fQ6XSULFmSunXr8uWXXxIUFMTt27f56quviI6O5ocffmDjxo352j5x4gRnzpzh5s2bMvL2lNNoNDRo0IAff/yRuLg4li9fjpubGy+//DKenp4MGTKErVu3SgIvhAXSaDQ0a9aMX375hfj4eBYuXIi1tTUjR47E09MTnU7Hrl27irxMqigK++LTHqr/fqzjAem5CisvJLM/Ps0ir2UrcLLWp08fAgMDGTlyJOnp6Y+07MXUqVPJyMgw2ta7d2++/fZbvv76a6pVq8acOXMICQmhdevWD2xHURQupmSTml3wX6ic7CzCNv5O9Xbd7/t8tXY9CNvwOznZWbQY8QoarRUz+jXni3ZVuBN/GddSXrR76T02f/853Wv60n/Uy4/9gWs0GlatWoW7uzstW7akffv2VKxYkeXLl6v7dOrUifHjx/Puu+/SoEEDkpOTGTFihFE7Y8eOxcrKiqpVq+Lh4UFsbCy3bt0CoG/fvnz22WfUq1ePuLg41qxZw4cffoirqyszZ87kxx9/5IcffqBWrVocPHgwX5I8evRoKleuTP369fHw8GDv3r2P9Z6F5bC3t2fAgAFs2LCBS5cuMXHiRI4dO0bHjh3x8fFRv6kLISyPo6Mjw4YNY9u2bVy4cIH333+f0NBQ2rRpg5+fHxMnTuTChQuFflxFUdgdl8ZfcYVXpXoYece2tITNIpeb2hdvug/6Xq28HGni6WjqMO6rd+/erF69mpiYGPXuWYAdO3bQrl07Fi5cyLBhw0wYobBEyn3WLmzatKm6duHDXB4hhDAviqIQGhqKXq9nxYoVpKSk0Lp1a3Q6HX379sXZ2fmxjyH996OxuGQtKjGTlRfM5xbkPr4uBLjZ/feOT1B0dDQBAQEYDAYSEhJwc3MD7t48ULt2bdzd3dmzZ899b24QoqAyMjKM1i7MW/5Kp9PRpk0boylkhBCWJTU1lZUrV6LX69mxYwdOTk7079+f4OBgWrRoYdR/5OTkMGzYMAYPHpxvhZx7Sf/96Czq1oj0HAMbY5/8RZD/ZmNsCuk59y/HLl68WJ3y5J+PatWqFVlM48ePx93dPd/2H374gcjISGbNmiWJmnhs9vb2DBw4kI0bN6prFx4+fJgOHTrg6+vLxx9/zNmzZ00dphDiETg5OTF8+HC2b9/OhQsXePfdd9m9ezetWrXCz8+Pzz77TL1hcOvWrSxfvpx+/fqxbdu2+7Znaf23ubGokbXVMUlEJmQV+cWID0MDBLrb0tMn/zV8ycnJXLt27b6vs7GxMSpPFpawsDDq1KnDzz//zOjRo9Xt165dIyAggKFDh/Ljjz8W+nGFgLtllL///hu9Xs+yZcu4c+cOzZs3R6fT0b9//0e61lUIYR4MBoNRmTQ1NZU2bdqQkpLC0aNHURQFW1tbdu3aRaNGjYxea2n9t7mxmGTN3IZP/6lvRRf8XU0/nNq1a1eio6M5ffo01tb/P41ecHAwa9euJSoqSpaMEk9Eeno6q1atQq/Xs3XrVuzt7enbt69aJtVqLWpgXwhxj5SUFFauXMnPP/9sdOOZVqvFxcWFvXv3qhUk6b8fn0X831JRFP6KS8NcC3ca4K+rpr+7ZPfu3WzcuJEvv/zSKFHbv38/er2eL7/8UhI18cTkrV24efNmYmNjGT9+PAcPHqR9+/b4+vryySefGK2EIoSwHM7OzowYMYIhQ4YYXVZjMBi4c+cOjRo1IiwsTPrvQmIRI2tXUrNZGGX+E3IOD3ClrJPNf+9YBBRFoWnTpuTk5HDw4EH1jyc3N5dGjRqpd/HJRd/ClBRF4cCBA2qZNCkpiRYtWqhl0nuXjxNCmL/69etz5MgRbGxsUBSFnJwc9bnOnTvz6+9rpP8uBAVebsqUjt5IR0Phz25cmDTcjdNUH/aaNWs4cOAA27ZtM/qWM3fuXI4cOcLevXslURMmp9FoaNKkCU2aNGHGjBlqmfT555/ntddeo2/fvgQHB9OqVSspkwphAVq1akW5cuUoV64cZcuWpWzZspQpU4ZSpUpRrVo1NlxKlf67EJj9yFpajoFZJ28XeDFXU9ICr9YojuMTXn8sNzeXmjVrUqZMGbZu3apuv337NgEBAXTr1o358+c/0ZiEeBiXLl1S1y48e/asWa1dKIR4NNJ/Fx7zjOoeF5OzLeKDBjAAsclPfjmmhQsXEh4ezuTJk422f/LJJ2RlZTF16tQnHpMQD6N8+fJ8+OGHnDlzJt/aha1atSIkJKRQlngTQjw50n8XHrNP1uLTcsw/yP/RcjfeJykjI4MJEybQv39/dT1RgOPHjzN79mw+/fRTPD09n2hMQjyqf65duGjRImxtbRk1ahReXl5PbO1CIcTjk/678Jh9GXTJ2URiU8z3BP6Tt7MNQ/yf3JI7M2fOZOzYsZw+fZrKlSsDdy/ibtmyJbdu3eL48ePY2JhvHV6IgoiNjWXBggXo9Xqio6Px8fFRy6S+vr6mDk8IcR/Sfxces056FUUx60z3fuLTcp7YLcBJSUl8+eWXjBw5Uk3UAJYsWUJoaCjff/+9JGriqeDt7a2uiLBnzx7atWvH9OnTqVixIq1bt2b+/PmkpJjX7OhCPMuk/y5cZp2speUoZFlYtSPLoJCW82Q+7OnTp5OSksKECRPUbcnJyYwbN45+/frRrl27JxKHEE+KRqOhefPm/Prrr8THx7Nw4UKsrKzQ6XR4enoSHBzM7t27pUwqhIlJ/124zDpZyzaY50n7LzlPIDO/fv0606dP5/XXX6ds2bLq9s8//5zExESmT59e5DEIYUpOTk4MGzaM7du3ExMTw3vvvcdff/1F69at8fPzY+LEierahUKIJ0v678Jl1smahX7W5D6BbxN5qxS899576rYzZ84wc+ZMPvjgA7y9vYs+CCHMRIUKFRg/fjznzp1j9+7dtGnThmnTpuHr60vbtm1ZsGABqamppg5TiGeG9N+Fy6yTNa25rk/xH6yK+KxeuHCB2bNn895776nLRymKwuuvv0758uUZN25c0QYghJnSaDS0bNmSuXPnEhcXp84vGBQUhKenJyNHjuSvv/4y2+tShHhaSP9duMw0rLtsLPTTttYUbdwTJkygZMmSvPHGG+q21atXs2XLFmbMmIG9vX2RHl8IS5C3duGOHTu4cOEC48aNY9euXbRq1Qo/Pz8+//xzLl68aOowhXgqSf9duMw6WXO01mBr1hHmZ6vV4GhddB/2yZMnWbRoEZ988gmOjo4ApKen89Zbb9GlSxd69OhRZMcWwlL5+PjwySefcO7cOXbt2kXLli2ZOnUqPj4+tGvXjoULF0qZVIhCJP134TLrU6nRaPB0tIjlS1WejtZGa3MWtg8//JBKlSoxatQoddtXX33FlStXmDlzZpEeWwhLp9Vq1RUR4uPj0ev1GAwGRowYgZeXF88//zyhoaFSJhXiMUn/XbjMOlkD8HK0Mf8g/0cLlCnCX87Q0FDWrVvHF198oc6fFhMTw5QpU3jnnXcICAgosmML8bRxdnYmKCiInTt3cv78ed5++222b99OixYt8Pf354svviA2NtbUYQphsaT/Ljxmv4JBREImq2MsZ03A3j4uVHG3K/R2FUWhRYsWpKWlcfjwYbTau38Cffr04eDBg0RGRuLs7FzoxxXiWWIwGPjrr7/Q6/X89ttvpKen065dO3Q6Hc8995x66YEQ4r9J/114zD7preBiOZl5bk42X73/Fjt37iz0STk3bNjA3r17mTx5spqobdmyhT///JNp06ZJoiZEIdBqtbRu3Rq9Xk98fDzz5s0jOzubYcOG4enpyejRo9m7d6+USYUoAEvqv7WAt4v5rvhj9iNrAGtjkghPyMKcA9WgkHv5LCFvB3Hu3DkqVKigrl1YsWLFx2o7NzeXOnXqUKJECXbs2IFGoyErK4uaNWvi6enJzp07zbbOLsTTIDo6mgULFjB//nwuXryIv78/Op2O4cOHU758eVOHJ4TZsoz+G6q629LDp5ipQ3kgi0h663o4mPUHDaCgIahtA6KioggNDaVDhw7MmDGDSpUqqd/UH3XtwqVLl3Ly5EmmTJmiJmXfffcdZ8+e5bvvvpNETYgiVqlSJSZOnMj58+fZsWMHTZo04csvv6RChQp07NiRJUuWkJaWZuowhTA7ltF/343TnFnEyJqiKMyLTORmRq5ZfugaoKS9FSOruBklTmlpaaxcuRK9Xs+OHTtwdHSkX79+6HQ6WrZsqZYz/01WVhaVK1emTp06rFy5EoC4uDgCAgIIDg7mu+++K6q3JYT4F8nJyfz222/o9Xr27NlDsWLFGDhwIMHBwTRu3Fi+RAmB5fbf5sYikjWAs3cy+eO8+V6o2LeiC/6uD74w8eLFiyxcuBC9Xk90dDS+vr4EBQUxYsQIfH19H/i677//njfffJNTp04RGBgIwPDhw9m0aRNRUVG4u7sX+nsRQjycc+fOqWXS2NhYAgIC1DJpuXLlTB2eECZl6f23ObCYZA1gdUwSkWZW+9YAge629CxgrVtRFEJDQ9Hr9axYsYKUlBRat26NTqejb9++RjcKJCcnU6lSJXr06MHcuXOBu9N3tGjRgl9//dVorjUhhOkZDAZ27tyJXq/njz/+ICMjgw4dOqDT6ejduzcODuZdahGiqDwN/bcpWcQ1a3k6lnPG3sq8hintrTR0KFfwOzE1Gg0tWrRg7ty5xMfHs2DBArRaLTqdDk9PT4KDg9W1C2fOnElSUhITJkwA7t5o8Nprr9GgQQOCg4OL6i0JIR6RVqtVV0SIj4/nl19+IS0tjSFDhuDl5cVLL73EgQMH5G5SYVF0Oh0ajQaNRoONjQ2lS5emQ4cOzJs3r8AzHzxM/x228Q8+rF+aP78cm++53ya8ysK3RxhtS7gaywd1Pbh65mSB2s9TkP779OnTDBgwAA8PD+zs7PD392f8+PFP/BpVi0rWHKy1dPE2rykqung742D9aKfRycmJ4cOHs337di5cuMC7777L7t27adWqFb6+vnzxxRcMGzYMb29vAH7++WfCwsKYNWtWga53E0KYTrFixRg1ahR79uwhKiqKV199lfXr19OkSRMCAwOZMmUKV65cMXWYQhRI586diYuLIyYmho0bN9KmTRveeOMNunfvTk5Ozn++/mH678Orl9Ay6FVObP6TrPSiS4r+q/8+cOAAjRo1Iisri/Xr1xMVFcWkSZOYP38+HTp0ICsrq8hi+yeLKoPm2Refxl9xpr/zqpWXI008C3eSTIPBQGhoKGPGjOH06dMAtGnThn79+vHRRx/Rp08ftSQqhLAsubm5RmXSrKwsOnbsiE6no1evXtjb25s6RCHy0el0JCYmsmrVKqPtO3bsoF27dvzyyy88//zz3Llzh3HjxrFq1SoyMjKoX78+M2bMoFatWuprps7/je+nfsm16EhsHZ3wrdOEYdP16vMJV2OZ0a85H24+xbxXBtB4QDB1uw8EYNtPX7H956+NYhj98yp+eaG30Tbfek154ZfV/DbhVTKSk6hQuxGhi34kJzubWh17033sl7T1dv3X/ltRFKpXr46joyN///230QDJ8ePHqVOnDpMnT+a99957yLP5aCxyeKZJaQealDbttR9NSjvQuAhi0Gq1+Pj4cPbsWT7++GPmz5+Poii88sorJCYmkp6ezp49e6SMIoQFsrKyon379ixatIj4+HjmzJlDUlISgwYNwsvLi5dffpm///5b/r6FRWjbti21atVi5cqVKIpCt27diI+PZ8OGDRw5coS6devSrl07bt++DcD69ev5aNRg2nfuwmtLdvD8T39QtmotozYPr15C5ebtsXcpRu2u/Ti8arH6XIsRY6jRoRcBTdvy4ZZTfLjlFN61GjBm4RYARs3+gw+3nGLYNL36mujDody+HMPzc1bRf+IsjqxdTuLuP/6z/w4LCyM8PJy33347XyWrVq1atG/fnqVLlz7O6XsoFpmsaTQaWno50srLNEu/tPJypFUZpyK7zffTTz/Fzc2N9957jxEjRjBt2jTg7jD0gQMHaNmypaxdKISFc3V15fnnn2fv3r2cOXOGMWPGsG7dOho3bky1atX46quvuHr1qqnDFOJfValShZiYGHbu3MnJkyf57bffqF+/Pv7+/kybNg03Nzd+//13AL788ksGDRpEyPRJ9G9WG6+A6rQZ9ZbalsFg4MjaZdTp2h+AWp2eI/bEYW7GngfAztEZG3t7rG3tcClZGpeSpbG2scXZvQQAjm7uuJQsjaPr/8+S4ODiRs/3plDK15/Alh1p0aEzl46E/mf/HRUVBaDOwvBPgYGB6j5PgkUma3A3YWvi6UgfXxccrDQU9W0HGsDBSkMfX5dCL33eKzw8nPnz5zN+/HicnZ0xGAy89tprVK9enbVr13Lu3Dl27dpFixYtmDJlCj4+Puo3dZmUUwjLFBAQwJdffklMTAxbtmyhTp06TJgwgfLly9O1a1dWrFhBRkaGqcMUIh9FUdBoNBw5coSUlBRKlCiBs7Oz+rhw4QLR0dHA3dGqdu3aPbD/Prt/J1npaQQ0aweAk3sJ/Bq35sjqJY8cX+lKlbGyslL77+q+5bh+/frjvm0URcHW1vax2yko811ivoAC3Owo72zDlsspRCRkoYFCvTU4r71Ad1s6lHv0mwkK6qOPPqJChQq88MILACxatIj9+/ezc+dOrK3vflytWrWiVatWfP/99/z+++/o9XqGDx/OmDFjGDBgAMHBwTRt2tSsJ/gTQuRnZWVFhw4d6NChA4mJiaxYsQK9Xs/AgQNxc3Nj8ODBBAcHU79+ffn7FmYhIiICX19fDAYDXl5e7Nq1K98+bm5uAPmmrvln/31kzRLS7yQwoam3uo9iMBB35iQdxnyA1srqoeOzsrYx6r81Gk2B7mD19/cH7g6g1K5dO9/zkZGRBAQEPHQ8j8piR9bu5WCtpZdPMfr4ulDS/u6H+bj/G8t7fUl7K/pWdKGnT7EiT9QOHDjAqlWr+Pzzz7G1teXOnTu8++67DBw4kNatW+fb39nZGZ1Ox65du4iOjubtt99m27ZtNG/eXP2mLmVSISyTm5sbL7zwAvv27SMyMpKXX36ZNWvW0LBhQ6pXr87XX39NXFycqcMUz7AdO3Zw8uRJ+vbtS926dYmPj8fa2ho/Pz+jR8mSJQGoWbMm27dvN2ojr/9u45pFxK5NDJ78M68v3clr9zwy01M5s/fu66ysbTHk5hq1YWVzd4RLyb2bhOX13/ZWWrwcrR+p/65Tpw5VqlRhxowZ+ZK748ePs23bNnQ63UO1+TieimQtT4CbHSOruDE8wJWq7rbqmyvom1T310A1dztGBLgysorbE5nZWFEU3n//fWrWrMngwYMB+Oyzz0hOTlavWfs3FStW5NNPP1XXLmzatCmTJk3Cx8eHDh06sHjxYimTCmGhKleuzKRJk7h48SKbN2+mZs2ajB8/nnLlytGtWzd+//13MjMzTR2meIplZmYSHx/PlStXOHr0KJMmTaJXr150796dESNG0L59e5o0aULv3r3ZvHkzMTEx7Nu3j48//pjDhw8DMGHCBJYuXcqECROIiIjg5MmTfPXVVwDsX7MCj5IlmPrKCNo2qEUZv0A8/QIpG1CNKi06cnj13RsN3MuUJ/5sODdizpGacIvc7Gyc3EtiY+9A1L7tpN6+jo9VBiMCXPF3tcXZ5tHSHI1Gw6+//kp4eDh9+/bl4MGDxMbG8ttvv9GjRw86derEiy++WDgntyCUp1hqdq4ScTtD2XE5RVkclahMD7uhTD6a/zE97IayOCpR2Xk5RYm4naGkZuc+8Vg3btyoAMr69esVRVGU06dPK9bW1sqkSZMeuc2kpCRl3rx5SsuWLRVAKVasmDJ69Ghl7969isFgKKzQhRAmkJCQoPz0009K48aNFUBxd3dXXnnlFeXQoUPy9y0KVVBQkMLdK4IUa2trxcPDQ2nfvr0yb948JTf3//vLpKQk5bXXXlPKlCmj2NjYKOXLl1eGDh2qxMbGqvv88ccfSu3atRVbW1ulZMmSSp8+fRRFUZQaNWooY8aMUfe7t/9+8/sFitbaWvlw62nlo+0Ril/j1oqto5MCKC//skpZHJWojP1qluJVtpyi1WqVVq1aqXH36tXL6L288cYb6vMFceLECaVv375K8eLF1XPw6quvKtnZ2Q9/Ih+DRc6z9qgURSEtRyFHUcg1gJUWrDUaHK01Jr3+w2AwUK9ePVxcXNi9ezcAHTp04OLFi5w6dQo7u8cf2YuOjmb+/Pnq2oX+/v7q2oXly5d/7PaFEKYTGRnJ/PnzWbBgAVevXqVatWrodDqGDRuGp6enqcMT4rGZQ/9tMBgYNWoUmzdvZvfu3ep1bU/CM5Wsmatly5YxePBgQkNDadasGb///jv9+/dn/fr1dO3atVCPZTAY2LVrF3q9nt9//52MjAzat29PcHCwrF0ohIXLzc1l69at6PV6Vq1aRU5ODl26dEGn09G9e/dC+eInxLPMYDDw/fff4+LiwsiRI5/YcSVZM7GsrCyqVq1K1apVWbNmDWlpaQQGBlKzZk3Wrl1bpMdOSkri999/JyQkhNDQUIoVK8agQYPQ6XQ0btxY7jYTwoIlJCSwfPly9Ho9f//9N8WLF2fIkCHodDrq1q0rf9/imbZnzx66dOnywOdTUlKeYDT/TZI1E5s9ezavvPIKJ06coHr16nzyySdMnTqV8PBwKlWq9MTiOHfunFomvXTpEgEBAeh0OkaMGEHZsmWfWBxCiMKXN3/jwoULiYuLo0aNGuh0OoYOHUrp0qVNHZ4QT1x6evq/rs3r5+f3BKP5b5KsmVBqaip+fn507NiR+fPnc/78eapWrcrYsWP54osvTBKTwWBg586dhISEsHLlSjIzM+nQoYO6dqGUSYWwXDk5OUZl0tzcXLp27aqWSZ/kJJ9CiIKTZM2EJk2axMSJEzlz5gw+Pj706tWLY8eOERERgZOTk6nD486dO/z222/o9Xr27t2Lq6urWiZt1KiRlFGEsGC3b99m2bJl6PV6Dh06RIkSJRg6dCg6nY7atWvL37cQZkSSNRO5desWFStWJDg4mJkzZ7Jx40Z1WZn+/fubOrx8zp49q5ZJL1++TJUqVdS7zaRMKoRlO336tFomjY+Pp2bNmmqZtFSpUqYOT4hnniRrJvLuu+8ye/ZsoqOjcXV1pUaNGpQvX55t27aZ9Tfa3NxcduzYgV6vZ+XKlWRlZdGxY0e1TGpvb2/qEIUQjygnJ4ctW7YQEhLCmjVrMBgMdO3aleDgYLp27SplUiFMRJI1E7h8+TL+/v689957fPrpp0yZMoWPP/6Y48ePU61aNVOHV2B37txR1y7ct2+funahTqejQYMGZp10CiH+3a1bt9Qy6eHDhylZsqRRmVQI8eRIsmYCo0ePZtWqVZw/f547d+5QpUoVRo8ezYwZM0wd2iM7c+aMOinnlStXCAwMVMukZcqUMXV4QojHcOrUKbVMeu3aNWrVqqWWST08PEwdnhBPPUnWnrDIyEiqVavGN998wxtvvMHgwYPZsWMHUVFRuLq6mjq8x5abm8v27dvR6/X8+eefZGVl0blzZ3Q6HT169JAyqRAWLDs7m82bN6PX61mzZg2KotC9e3d0Oh1du3bFxsbG1CEK8VSSZO0J69+/PwcPHiQqKooDBw7QunVrQkJC0Ol0pg6t0CUmJqpl0v379+Pu7q6WSevXry9lUiEs2K1bt1i6dCl6vZ4jR47g4eGhlklr1apl6vCEeKpIsvYEHTp0iIYNG6LX6xk6dCh169bFycmJvXv3otVqTR1ekfrn2oVVq1ZVy6ReXl6mDk8I8RhOnDjB/PnzWbRoEdevX6d27doEBwczZMgQSpYsaerwhLB4kqw9Qe3btyc+Pp7jx4/z448/8sYbb3Do0CHq1atn6tCemNzcXLZt26aWSXNycozKpLJ2oRCWKzs7m02bNqHX69Xl8vLKpF26dJEyqRCPSJK1J2Tbtm106NCB1atX07hxYypXrsyAAQOYM2eOqUMzmX+uXeju7q6uXVivXj0pkwphwW7evMmSJUvQ6/UcO3aMUqVKMWzYMHQ6HTVq1DB1eEJYFEnWngCDwUDDhg2xs7MjNDSU0aNHs3LlSqKioqRE8D8RERFqmTQuLo7q1aurd5t5enqaOjwhxGM4fvy4Wia9ceMGdevWRafTMXjwYPl/oBAFIMnaE/Dbb78xYMAA/vrrL+zs7GjcuDGzZs1izJgxpg7N7Nxv7cIuXbqoaxdKmVQIy5Wdnc3GjRvVMqlGo6Fnz57odDo6deokZVIhHkCStSKWnZ1NtWrV8Pf3Z+3atTRu3JisrCyOHDmClZWVqcMza7dv31bLpAcPHqR48eLq3WZ16tSRMqkQFuzGjRtqmTQsLIzSpUurZdLq1aubOjwhzIoka0Xsl19+4YUXXiAsLIwjR44watQo/vrrL1q0aGHq0CxKeHg4er1eXbuwRo0aapm0dOnSpg5PCPEYwsLC1DLpzZs3qVevnlomLVGihKnDE8LkJFkrQmlpafj7+9O6dWt++OEHAgIC6NixI4sWLTJ1aBYrb+1CvV7P6tWryc3NVdcu7Natm6xdKIQFy8rKYsOGDej1etavX49WqzUqk1pbW5s6RCFMQpK1IvTVV1/x0UcfcebMGb799lvmzZvHmTNnZPmlQnL79m2WLVtGSEgIhw8fpkSJEkZrF0qZVAjLdf36dZYsWUJISAgnTpzA09NTLZNa0hrKQhQGSdaKSEJCAhUrVmTo0KG8+OKL1KlTh8mTJzNu3DhTh/ZU+ufahTVr1lTLpKVKlTJ1eEKIxxAWFkZISAiLFy/m1q1bNGjQAJ1Ox6BBgyhevLipwxOiyEmyVkQ++OADvvvuO86dO8fgwYOJj4/nxIkTUqYrYjk5OerahatXr0ZRFLp166auXSjnXwjLlZWVxfr169UyqZWVFb169UKn09GxY0cpk4qnliRrReDq1av4+fnxzjvvUL16dQYNGsTmzZvp2LGjqUN7pvxz7cKSJUsalUmFEJbr2rVrLF68mJCQEE6dOoWXlxfDhw8nKCiIqlWrmjo8IQqVJGtF4OWXX2bFihWcOHGCRo0a0aBBA/78809Th/VMO3nypFomzVu7UKfTMWTIEDw8PEwdnhDiESmKwrFjx9Dr9SxevJjbt2/TsGFDtUzq7u5u6hCFeGySrBWys2fPEhgYyNSpU7l16xYzZswgPDwcX19fU4cmuDvv3ebNmwkJCWHt2rUoiqKuXdi1a1eZlFMIC5aZmcn69esJCQlh48aNWFtb06tXL4KDg+nQoYPMbSksliRrhWzQoEHs3buXjRs3UrduXT788EM+/fRTU4cl7uPmzZtqmfTo0aN4eHiod5vVrFnT1OEJIR5DfHy8WiY9ffo0ZcqUUcukgYGBpg5PiIciyVohOnr0KPXq1WPu3Ln88ccfnD59moiICBwcHEwdmvgPJ06cUCflvH79OnXq1FHLpLJ2oRCWS1EUjh49il6vZ8mSJdy+fZtGjRqpZVI3NzdThyjEf5JkrRB17tyZixcvMmXKFHr37s3KlSt57rnnTB2WeAj3W7uwe/fuBAcH07lzZymTCmHBMjMzWbt2LXq9nk2bNmFtbc1zzz2HTqejffv2UiYVZkuStUKyc+dO2rZty9KlS/noo4+oVKkSmzdvlolZLdiNGzfUMumxY8coVaqUWiatUaOGqcMTQjyGuLg4tUwaHh5O2bJlGT58ODqdjsqVK5s6PCGMSLJWCBRFoXHjxgD06NGDiRMncvLkSapUqWLiyERhOX78OHq9Xl27sG7duurahVImFcJyKYrCkSNHCAkJYenSpSQkJNCkSRN0Oh0DBw7E1dXV1CEKIclaYfjzzz/p06cPS5cuZeTIkbzyyit8/fXXpg5LFIGsrCy1TLpu3To0Go26dmHnzp1lUk4hLFhGRoZRmdTW1lYtk7Zr107KpMJkJFl7TDk5OdSoUQNvb29cXV0JDQ0lMjKSYsWKmTo0UcRu3LihllFOnDhB6dKl1TJp9erVTR2eEOIxXL16Vf37joiIoFy5cowYMYKgoCACAgJMHZ54xkiy9pjmzZvHqFGjmD17Ni+//DILFy5k2LBhpg5LPGFhYWHqpJw3b96kXr16BAcHM3jwYFm7UAgLpigKhw4dQq/Xs3TpUhITE2natCk6nY4BAwZImVQ8EZKsPYaMjAz8/f1p0qQJp0+fxt3dnT179shNBc+wrKwsNmzYQEhIiLp2YV6ZtFOnTlImFcKCZWRksGbNGvR6PZs3b8bW1pY+ffoQHBxMmzZtpEwqiozW1AFYsh9//JG4uDj8/PyIjIxk1qxZkqg942xtbenduzerV6/mypUrTJkyhTNnztC9e3fKly/PuHHjOH36tKnDFEI8Ant7ewYMGMCGDRuIjY3l008/5ejRo3To0AFfX18+/vhjzp49a+owxVNIRtYe0Z07d6hYsSLdu3dn1apVDB06lB9//NHUYQkzpCiKUZn01q1bNGjQQJ2UU8qkQlguRVE4ePCgWia9c+cOzZo1Izg4mP79+8v1y6JQyMjaI5o2bRrp6elkZGRgY2PDF198YeqQhJnSaDTUqVOHb7/9litXrvDHH3/g6enJ66+/jpeXl/pNPScnx9ShCiEekkajoVGjRsyePZu4uDiWLl2Ks7Mzo0ePxtPTk+HDh7N9+3YMBoOpQxUWTEbWHsG1a9eoWLEiffv2ZeHChfz000+8+OKLpg5LWJhr166pd5udOnUKLy8vde3CqlWrmjo8IcRjuHz5MosWLUKv13PmzBm8vb0ZMWIEOp2OSpUqmTo8YWEkWXsEr776KosXL6ZChQpYWVlx8OBBubBUPLL7rV3YsGFDtUzq7u5u6hCFEI9IURT+/vtvQkJCWLZsGUlJSbRo0QKdTkf//v1xcXExdYjCAkiy9pDOnz9P5cqV6dmzJytXrmTfvn00adLE1GGJp0RmZibr1q1Dr9ezceNGrK2t6d27Nzqdjg4dOsiXAiEsWHp6OqtWrUKv17N161YcHBzo27cvOp2O1q1bo9XKlUni/iRZe0jDhg1j+/btZGVl0aNHD/R6valDEk+p+Ph4tYxy+vRpypQpo65dKEuZCWHZLl++zMKFCwkJCeHs2bNUqFCBoKAgRowYIWVSkY8kaw/h+PHj1KlTh5YtW3L06FGioqLw9PQ0dVjiKZe3dmFemTQhIYHGjRuraxe6ubmZOkQhxCNSFIX9+/ej1+tZvnw5SUlJtGzZEp1OR79+/aRMKgBJ1h5Kt27dOH36NLGxsUybNo23337b1CGJZ0xmZqa6duHGjRuxsbFR1y5s3769lEmFsGBpaWlqmXTbtm04OjrSr18/dDodLVu2lDLpM0yStQL666+/aNWqFVWqVEGj0XD8+HFsbGxMHZZ4hsXFxbFo0SJ17cKyZcuqZdLKlSubOjwhxGOIjY1l4cKF6PV6zp07h4+PD0FBQQQFBeHr62vq8MQTZhZp+qeffkrt2rULvd2YmBg0Gg1hYWEP3GfXrl1oNBoSExMB0Ov1+cpKiqLwwQcf4OvrS2RkJN9//70kasLkvLy81BURDh48SK9evfjpp5+oUqUKTZs25eeff+bOnTumDlMI8Qi8vb356KOPiIqKIjQ0lPbt2/PNN99QsWJFWrduzfz580lJSTF1mOIJeahkTafTodFo8j06d+5cVPE9cQMHDiQqKspo29q1a9m3bx937tyhX79+tGvXzkTRPZhOp6N3795F0nZRJdOicGg0Gho0aMAPP/xAXFwcy5cvx83NjZdffhlPT0+GDBnCli1byM3NNXWoQoiHpNFoaNasGb/88gvx8fEsXLgQa2trgoOD8fT0JDg4mN27d8uku0+5hx5Z69y5M3FxcUaPpUuXFkVshU5RFFKzDSRm5nI7I5fEzFzSc4x/wR0cHChVqpT6c25uLh9++CHe3t6kp6czffr0Jx22EAV279qFly5dYuLEiRw7doxOnTrh4+OjflMXQlgeR0dHhg0bxrZt27hw4QLvv/8+e/bsoXXr1vj5+TFx4kRiYmJMHWaRuF//nZpt4Fm5kuuhkzU7Ozs8PT2NHvdO2qnRaJgzZw7du3fH0dGRwMBA9u/fz7lz52jdujVOTk40adKE6OjofG3PmTOH8uXL4+joSP/+/dXSZJ6QkBACAwOxt7enSpUq+dbiPHjwIHXq1MHe3p769etz4PARAA5dT2fJ2URmnLjFKz8txy8ggNJuztRr3op5e+8uqv1b9B12Xkll0g+/GJVB+/Xrx+nTp7l06RJ2dnbUqFGDQYMGkZycrO6TnJzM0KFDcXJywsvLixkzZtC6dWvefPPNAp3ThIQERowYgbu7O46OjnTp0sVoMeD7jWzNnDkTHx8f9fn58+ezevVqdbRz165dahl42bJlNG3aFHt7e6pVq8auXbvUdu5X9l21apW6IL1er2fixIkcP35cbVumK7EMZcqU4d133yU8PJwDBw7QvXt3fvjhBypXrqx+U5cyqRCWqUKFCurC8Xv27KFt27ZMmzYNX19f2rZty4IFC0hNTTV1mI8sLcdAREImO6+kqv3396du81N4Aj9HJPBTeALfn7rNjBO3WHI2kZ1XUolIyCQt5+kcYSySa9Y+//xzRowYQVhYGFWqVGHIkCG8+OKLfPDBBxw+fBi4uwrAvc6dO8eKFStYu3YtmzZtIiwsjFdeeUV9/pdffuGjjz7iyy+/JCIigkmTJjF+/Hjmz58PQGpqKt27d6dy5cps2HOAri+/yytvjQXg9O0MYlNyuH71CovHBlO5WXteX7qT+r2Hsen7zwG4nJrDoevpHLuZQWauwtqYJGIS0ti5cycajQYHBwe2bdvGunXr2L17N1OmTFFje/vtt9m7dy9r1qxh69at7Nmzh6NHjxb4fOl0Og4fPsyaNWvYv38/iqLQtWtXsrOzC/T6sWPHMmDAAKNRz6ZNm6rPjxs3jnfeeYdjx47RtGlTevbsya1btwrU9sCBA3nnnXeoVq2a2vbAgQML/N6E6d27dmF8fDzLli2jWLFivPTSS3h6ejJ06FC2bdsmZVIhLJBGo6F58+b8+uuvxMfHs2DBAjQaDUFBQXh6ejJy5Ej++usvixiBUhSFK6nZrI1JYtbJ26yOSebQ9XRiU3LIekAOlmWA2JS7/ffqmGRmnbzN2pgkrqRmW8R7LqiHTtbWrVuHs7Oz0ePzzz832ic4OJgBAwYQEBDAe++9R0xMDEOHDqVTp04EBgbyxhtvGI3uAGRkZDB//nxq165Ny5Yt+f7771m2bBnx8fHA3QRw+vTp9OnTB19fX/r06cNbb73FnDlzAFi8eDHZObm0eHc6B63L4VS3LS1H3E328j7jv38LoXjZCnQf+wUePn7U6dqPej0GqTHc+7sQnpDFspg0qnR4DjRa5s6dS7169WjRooW6MC/cHVWbP38+06ZNo127dlSvXp2QkJACd3xnz55lzZo1/Prrr7Ro0YJatWqxePFirly5wqpVqwrUhrOzMw4ODkajnra2turzr776Kn379iUwMJDZs2fj6urK3LlzC9S2g4MDzs7OWFtbq207ODgU6LXC/Njb2zNw4EA2btxIbGwsEyZM4MiRI3To0AFfX1/1m7oQwvI4OTmp/dOFCxd499132b17N61atcLPz4/PP/+cixcvmjrM+4pKzGRuZCILo+4QnpCl9scFHSe7d//whCwWRt1hXmQiZ+9kFn6wJvDQyVqbNm0ICwszetw7AgZQs2ZN9d+lS5cGoEaNGkbbMjIySEpKUrd5e3tTrlw59ecmTZpgMBg4c+YMN27c4NKlS4waNcooSfziiy+Ijo4mPcfAur+PU7xSVVI0dgAogHfN+kZxXY85S/ka9dQSH4B3zQb3fZ95+bhz8ZJ4+Pjh0Kiren2bl5cX169fB+4uP5WdnU3Dhg3V17q6uhZ46oSIiAisra1p1KiRuq1EiRJUrlyZiIiIArXxX+5dDsva2pr69esXWtvCcpUtW5b333+fiIgI9u/fT9euXZk1axYBAQE0b96cuXPnGv2NCiEsh4+PD+PHj+fcuXNqwjZ16lR8fHxo164dCxcuNIsyaXqOgdUxSay8kMytjLuDHI87Hpb3+psZufxxPpnVMUn5rk+3NA+drDk5OeHn52f0KF68uNE+905rkZcY3W/bv929krePRqNR9/vll1+MksRTp06xZMtufg5P4HZ6DvAfH/IjDYlqsLK2JjIxi5/DE4hKzDSKKW+Y9d4E8N7t/+VB+ymKorap1Wrz7VfQEumDFGXbwrJoNBoaN27MTz/9pN4w5OTkxOjRo/H09FS/qcvdZkJYHo1GQ8uWLZk3bx7x8fHMnz8fRVEYMWIEXl5ePP/884SGhpqkZBiVmMnP4QlEJmQBj5+k/VNee5EJ/99/WyqzmGcN7k4AePXqVfXn/fv3o9VqCQgIoHTp0pQtW5bz58+rCWKlSpW47lyGQ4aSpOcqlKpYmbizp8nOSP//Nk8cMTpGKd8ALp003hZ78nCB4lOA9FyFlReSiUnKUrdXqlQJGxsbDh48qG5LSkoqcCmpatWq5OTk8Pfff6vbbt26RVRUFIGBgQB4eHgQHx9v9Mf0z7njbG1tH1h6PXDggPrvnJwcjhw5oq4t6eHhQXJystE3rIdpWzxdHBwcGDRoEJs3byY2Npbx48dz8OBB2rdvj6+vL+PHj7/vzUFCCPPn7OzMiBEj2LFjBxcuXOCdd95hx44dtGjRAn9/f7744gtiY2OLPA5FUdgXn8bKC8mk5yqFnqTlOx7/33/vj0+zyGvZHjpZy8zMJD4+3uhx8+bNxw7E3t6eoKAgjh8/zp49e3j99dcZMGCAuvbmp59+yuTJk/n22285c+YMITsO8s1Pv7Jn0WwAanXpg1aj5Y/P3uTa+TNEhm5lz8IfjI7RqJ+OW5djWDd9PDdizhG28Q+Orl320LGeS8oiI1dBURRcXFwICgpi3Lhx7Ny5k9OnTzNy5Ei0Wm2+0bb78ff3p1evXowePZrQ0FCOHz/OsGHDKFu2LL169QKgdevW3Lhxg6+++oro6Gh++OEHNm7caNSOj48PJ06c4MyZM9y8edNodOyHH37gzz//JDIykldeeYWEhARGjhx595w0aoSjoyMffvgh586dY8mSJfnu9vTx8eHChQuEhYVx8+ZNMjMt99uJKLhy5crxwQcfEBkZyb59++jcuTPfffcdfn5+6jf1e++KFkJYDh8fHyZMmMC5c+fYtWsXLVq0YMqUKfj4+NChQwcWL15MWlpaoR9XURR2x6XxV1zht10Qece2tITtoZO1TZs24eXlZfRo3rz5Ywfi5+dHnz596Nq1Kx07dqR69epGU3M8//zz/Prrr+j1emrUrMmrfTpzdO0yipfxBsDO0ZkRMxdx/fwZvh/cli0/TKLz658YHcPNqxxDvw4h8q/NfDeoNX//rqfjKx89UryZuQoHrt0dxfvmm29o0qQJ3bt3p3379jRr1kydYqQgQkJCqFevHt27d6dJkyYoisKGDRvU0nFgYCA//vgjP/zwA7Vq1eLgwYOMHTvWqI3Ro0dTuXJl6tevj4eHB3v37lWfmzJlClOnTqVWrVrs2bOH1atXU7JkSQCKFy/OokWL2LBhAzVq1GDp0qV8+umnRm337duXzp0706ZNGzw8PCxmXj1RODQaDU2aNGHOnDnExcWxePFi7O3tef755/H09FS/qUuZVAjLo9VqadWqFSEhIcTHxxMSEkJ2djbDhg3D09OT0aNHs3fv3kJLbvZfS1f7TlMxhxgelsWtDRqVmMnKC+bzbb6PrwsBbnZG21JTUylbtizTp09n1KhRJors7nJbvr6+HDt2TFYgEIXu0qVL6tqFZ8+epUKFCgQFBTFixAgqVapk6vCEEI/h/PnzLFiwgPnz5xMTE4O/v7/6912+fHmjfRVFYfz48fTs2dPoZrt/soT+21xZVLKWnmPg5/AE0nPNJ2QHKw0NM2OIORdFw4YNuXPnDp999hm7du3i3Llz6giWKUiyJp4ERVHYv38/ISEhLF++nOTkZFq2bIlOp6Nfv364uLiYOkQhxCMyGAz89ddfhISE8Pvvv5Oenk779u3VJQ4dHR0JDQ2lRYsWuLi4EBoaajQjRB5z7b9fqOqOg7XZXL7/QOYf4T22XE4hw4w+aICMXIW/r6cxbdo0atWqRfv27UlNTWXPnj2ULFmSPXv25JuX7t6HEJZOo9HQtGlTde3CRYsWYWtry6hRo/Dy8kKn07Fr1y4pkwphgbRarbpwfHx8PHPnziUzM5OhQ4fi5eXFCy+8wNSpU7G2tiYtLY127drd9yYkc+2/t15OMXUYBWIxI2vmNnz6T30ruuDvmn84NT09nStXrjzwdX5+fkUZlhAmExsbq5ZJz507h4+PD0FBQQQFBeHr62vq8IQQjyE6OpoFCxYQEhLCpUuX1O1WVlaUKVOGAwcOUKZMGcBy+29zYhHJmqIozI1M5FZGbpHf4vsoNEBJeytGVnEr0B2gQjxLFEVh79696PV6li9fTkpKCq1atVLLpDLCLITlWrBgAUFBQUbbNBoNXl5eHD58GE9PT+m/C4FFlEGvpuVw00w/aLg7h8uNjFyupuWYOhQhzM4/1y5cuHAh1tbWBAcH4+npSXBwMLt375YyqRAWKCQkRP23tbU1VlZWKIrC1atXCQoKkv67kFjEyNramCTCE7LM9sOGu9l5VXdbevgUM3UoQliEixcvsmDBAvR6PefPn8fX11e920zKpEJYhj59+hAeHk6FChUoX748ZcuWpUyZMtja2tKtWzf+TrOX/rsQmH2ylpZjYNbJ2wVezNWUtMCrNYrjaAF3lghhLhRFITQ0FL1ez4oVK0hJSaFNmzbodDr69u2Lk5OTqUMUQjwC6b8Lj3lGdY+LydkW8UEDGIDYZFlXU4iHodFoaNGiBXPnzlXXLgQICgrC09OTkSNH8tdff1ncjONCPOuk/y48Zp+sxaflmH+Q/6PlbrxCiEfj5ORktHbhuHHj2LVrF61atcLPz4/PP/+cixcvmjpMIUQBSP9deMz+PMalWVZmbu4XKQphKXx8fPjkk084d+4cu3fvpmXLlkydOhUfHx/atWvHwoULSU1NNXWYQogHkP678Jh1sqYoillnuvcTn5Yj5RohCpFWq6Vly5bq2oV6vR6DwcCIESPw9PRk1KhRhIaGyt+dEGZE+u/CZdbJWlqOQpalpOX/k2VQSMsxzw9bCEvn7OxMUFAQO3fu5Pz584wdO5YdO3bQokUL/P39+eKLL4iNjTV1mEI886T/LlxmnaxlG8zzpP2XHDPNzIV4mvj6+jJhwgSio6PZuXMnzZs3Z/Lkyfj4+NC+fXsWLVpEWlqaqcMU4pkk/XfhMutkzUI/a3It7NuEEJYsb+1CvV6vrl2Yk5PD8OHD8fT0ZPTo0ezdu9dsyxtCPI2k/y5cZp2sac135Yd/ZWXWZ1WIp5eLiwvBwcHs2rWLc+fO8dZbb7F161aaN29O5cqV+fLLL43WMRRCFA3pvwuXmYZ1l42FftrWZry+mBDPikqVKjFx4kTOnz/Pjh07aNKkCZMmTaJChQp07NiRJUuWSJlUiCIi/XfhMutkzdFag61ZR5ifrVaDo7V5fthCPIu0Wi1t2rRh/vz5xMfH8+uvv5KRkcHQoUPx8vLihRdeYN++fVImFaIQSf9duMz6VGo0GjwdrU0dxkPxdLRGY6aZuRDPOhcXF3VFhLNnz/LGG2+wefNmmjVrRpUqVZg8eTKXL182dZhCWDzpvwuXWSdrAF6ONuYf5P9ogTIW9sspxLPKz8+Pzz77jAsXLrBt2zYaNmzI559/jre3N506dWLp0qWkp6ebOkwhLJb034XH7M+jp6O1Rc2AbGnfJIR41mm1WnVFhPj4eH755RfS0tIYMmQIXl5evPTSSxw4cEDKpEI8JOm/C4/ZJ2sVXCwrM/d2sTF1GEKIR1SsWDFGjRrFnj17iIqK4tVXX2X9+vU0adKEwMBApkyZwpUrV0wdphAWQfrvwmP259HRWkuguy3mWUX+fxog0N0WR2uzP6VCiALIWxEhJiaGrVu3Ur9+fSZOnIi3tzedO3dm+fLlZGRkmDpMIcyW9N+Fx3wju0ddDwfMvQChcDdOIcTTxcrKSl0RIT4+njlz5pCcnMygQYPw8vLi5Zdf5u+//5YyqRD3If134bCIZK2MozUe9lZmm51rAA97K7O+OFEI8fhcXV15/vnn2bt3L2fOnGHMmDGsW7eOxo0bU7VqVaZOncrVq1dNHaYQZkP678KhUSzk6+DZO5n8cT7Z1GE8UN+KLvi72pk6DCHEE5abm8uOHTvQ6/WsXLmSrKwsOnXqhE6no2fPntjb25s6RCFMSvrvx2cRI2sA/q52Zln71gBV3W3N/oMWQhQNKysrOnTowOLFi4mLi2P27NkkJiYycOBAvLy8GDNmDAcPHpQyqXhmWVr/vWvXLjQaDYmJif/Zhl6vx83NrUjiu5fFJGsAHcs5Y29lXh+3vZWGDuWcTR2GEMIMuLm5qSsiREZG8vLLL7NmzRoaNWpE9erV+frrr4mLizN1mOIppNPp0Gg0aDQarK2t8fb25uWXXyYhIcEk8ezbtw8rKys6d+4MSP/9uCwqWXOw1tLF27xObBdvZxzM+A4SIYRpVK5cmUmTJnHx4kU2b95MzZo1GT9+POXKlaNbt2789ttvZGZmmjpM8RTp3LkzcXFxxMTE8Ouvv7J27VrGjBljkljmzZvHa6+9RmhoKLGxsdJ/PybLiPIeAW52tPRyNHUYALTyciTATcqfQogHs7KyomPHjixdupT4+Hh+/PFHbt++zYABA/Dy8uLVV1/l8OHDUiYVj83Ozg5PT0/KlStHx44dGThwIFu2bAHAYDDw2WefUa5cOezs7KhduzabNm1SXxsTE4NGo2HZsmU0bdoUe3t7qlWrxq5du4yOER4eTteuXXF2dqZ06dIMHz6cmzdvGu2TmprKihUrePnll+nevTt6vR74//77/OG9fFDXg8g9W/h2YGvGNy7HDyM6EX82XG3jyJqlTGxZidM7NzCtdyPGNy7H3Jf7kRhvPM9hxO7NfD+kHeMbl+OrHvXZNudrcnNy1Oc/qOvBoT8XsvCdID5p6s20Xg0J373JqP/esGEDAQEBODg40KZNG2JiYh75M7h16xYNGzakZ8+e6tQ+/2xfr9erZdY7d+4UqF2LS9YAmpR2oElp095m26S0A41NHIMQwrK4ubnx4osvsn//fiIiInjxxRf5888/adCgATVq1GDatGnEx8ebOkzxFDh//jybNm3CxubuRK/ffvst06dPZ9q0aZw4cYJOnTrRs2dPzp49a/S6cePG8c4773Ds2DGaNm1Kz549uXXrFgBxcXG0atWK2rVrc/jwYTZt2sS1a9cYMGCAURvLly+ncuXKVK5cmWHDhhESEqJ+GWlS2oGq7rYAbJw5ka5vfcorC7fg7F6SBW8NJzc7W20nOyOdnXNn0H/iLF6at56M1GSWfTBafT5q3w6Wj3+ZpoNH89bvoTz30TSOrl3GzrkzjOLZ/vM0anToxevLdlG5eXt+//hlAmzuLiV36dIl+vTpQ9euXQkLC+P555/n/ffff6RzfvnyZVq0aEGVKlVYuXIl9vb2/9m+q6trgdq2yGRNo9HQ0suRViYaYWvl5UirMk5mu+CrEML85S0cHxsby8aNG6levToff/wx5cqVo3v37vzxxx9SJhUPZd26dTg7O+Pg4EClSpUIDw/nvffeA2DatGm89957DBo0iMqVKzN16lRq167NzJkzjdp49dVX6du3L4GBgcyePRtXV1fmzp0LwOzZs6lbty6TJk2iSpUq1KlTh3nz5rFz506ioqLUNubOncuwYcOAu6XZlJQUtm/fDtztv2uVuHuHdLsXxuLfuDWe/lXp/9ksUm7f4PTO9Wo7uTnZ9HxvChVqNaBs1Vr0nziLi8cPcenUUQB2zp1Ba93r1OsxiOLlfPBv3JoOL7/PwT/mG72nuj0GUbtzH0p6V+TzL74kIy2VQ4cOqe+pYsWKzJgxg8qVKzN06FB0Ot1Dn/uoqCiaNWtG+/btmT9/PtbW1oXavnlPLPIvNBoNTTwdKWFvxcbYFDJylSKdeE/D3YsRu3g7S+lTCFFo8i7C7ty5MwkJCSxfvpyQkBD69etH8eLFGTJkCDqdjrp168oXRPGv2rRpw+zZs0lLS+PXX38lKiqK1157jaSkJK5evUqzZs2M9m/WrBnHjx832takSRP139bW1tSvX5+IiAgAjhw5ws6dO3F2zn/tWXR0NAEBAZw5c4aDBw+ycuVKtY2BAwcyb9482rdvD6D+Hr/QrTXHFQ0ZuQqOru6UrFCJ6xf+f6RPa21Nuaq11Z9L+fpj7+LK9QtRlK9elysRJ7gcHmY0kmYwGMjJzCArPQ1bh7sDOl7+VXG4p/92cXHh+vXrAERERNC4cWOjv617z0FBpKen07x5cwYPHsy3335r9FxhtA8WnKzlCXCzo7yzDVsupxCRkIUGCjVpy2sv0N2WDuUs52JEIYTlcXd356WXXuKll14iPDyc+fPns3DhQmbNmkX16tXR6XQMGzaM0qVLmzpUYYacnJzw8/MD4LvvvqNNmzZMnDiRcePGAeRL9hVFKdAXgLx9DAYDPXr0YOrUqfn28fLyAu6OquXk5FC2bFmj49jY2JCQkIC7u7u6vaKrHY3KuKv99/1i5D7x5e2jKAbav/gu1dp2y7ePtZ29OlVIBTcHXqjqrvbfGo0Gg8Ggxva47OzsaN++PevXr2fcuHGUK1dOfa6wrkV9KjIPB2stvXyK0cfXhZL2VgCPPZ9L3utL2lvRt6ILPX2KSaImhHhi8lZEiI2NZcOGDQQGBvLhhx9StmxZevbsqU7AK8SDTJgwgWnTppGSkkKZMmUIDQ01en7fvn0EBgYabTtw4ID675ycHI4cOUKVKlUAqFu3LqdPn8bHxwc/Pz+jh5OTEzk5OSxYsIDp06cTFhamPo4fP06FChVYvHhxvmPl9d/t3HO4FXseDx8/tf815ORwJTxM3f9GzDkyku/g4eMPQNkqNbh58RwlvSsaPTy8K6LVatV8oGEpxwf231WrVjV6z/88BwWh1WpZuHAh9erVo23btkarmBRG+/CUJGt5AtzsGFnFjeEBrlR1t1XfXEHfpLq/Bqq52zEiwJWRVdxkwlshhMlYW1vTpUsXVqxYQVxcHN999x3x8fH07duXMmXK8Prrr3Ps2DG5m1Tk07p1a6pVq8akSZMYN24cU6dOZfny5Zw5c4b333+fsLAw3njjDaPX/PDDD/z5559ERkbyyiuvkJCQwMiRIwF45ZVXuH37NoMHD+bgwYOcP3+eLVu2MHLkSHJzc1m3bh0JCQmMGjWK6tWrGz369eunXvuW57PPPmP79u2cOnWKL954EU+Pknz5wmCq/m8CXStrG9Z+9QGxJ49wJeI4v3/6OuVr1Kd89boAtB09lqPrV7Dtp6+4Hh3J9fNRnNzyJ0fnTlX77//y0ksvER0dzdtvv82ZM2dYsmSJevfqw7CysmLx4sXUqlWLtm3bqjcKFVb7KE+x1OxcJeJ2hrLjcoqyOCpRmR52Q5l8NP9jetgNZXFUorLzcooScTtDSc3ONXXoQgjxr06dOqWMGzdO8fT0VAClZs2ayjfffKNcu3bN1KEJEwgKClJ69eqVb/vixYsVW1tbJSYmRpk4caJStmxZxcbGRqlVq5ayceNGdb8LFy4ogLJkyRKlUaNGiq2trRIYGKhs377dqL2oqCjlueeeU9zc3BQHBwelSpUqyptvvqkYDAale/fuSteuXe8b35EjRxRAOXLkiLJz504FUNauXatUq1ZNsbW1VRo0aKCEhYWp+//061zFpZir8unPi5VS5X0UKxtbpVKDFsq7644a9d+jf1yu+NdtpNjZOyjOLsWU+g0aKj///LPaDqD8+eefRrG4uroqISEh6s9r165V/Pz8FDs7O6VFixbKvHnzFEBJSEj4z/MeEhKiuLq6qj9nZ2crffr0UQIDA9W/xcdpP4/FrA1aGBRFIS1HIUdRyDWAlRasNRocrTVy4a4QwiLl5OSwefNm9Ho9a9aswWAw0LVrV3Q6Hd26dcPW1tbUIQoLEBMTg6+vL8eOHaN27dpFeqxdu3bRpk0bEhISHrhUk16v580331SXfHqa+u+CvP9/eqrKoP9Fo9HgZKPF1daK4vZWuNpa4WSjtbgPWggh8lhbW6srIly9epWZM2dy9epV+vTpQ9myZXnzzTcJCwszdZhCPJZnvf9+ppI1IYR4mpUoUYJXXnmFQ4cOcfLkSYKCgli2bBl16tRR59S6ceOGqcMUwmJ06dIFZ2fn+z4mTZr0xOJ4psqgQgjxrMnOzjYqkyqKQvfu3dHpdHTt2lWd4V4Ikd+VK1dIT0+/73PFixenePHiTyQOSdaEEOIZcevWLZYuXYper+fIkSN4eHioM6rXqlXL1OEJIR5AkjUhhHgGnThxgvnz57No0SKuX79O7dq10el0DB06lJIlS5o6PCHEPSRZE0KIZ1h2djabNm1Cr9ezdu1aALVM2qVLFymTCmEGJFkTQggBwM2bN9VJO48dO4aHhwfDhg0jODiYGjVqmDo8IZ5ZkqwJIYTI5/jx42qZ9MaNG9StWxedTsfgwYOlTCrEEybJmhBCiAfKzs5mw4YN6PV61q1bh0ajoUePHuh0Ojp37ixlUiGeAEnWhBBCFMiNGzdYsmQJISEhHD9+nNKlSzNs2DB0Oh3Vq1c3dXhCPLUkWRNCCPHQwsLC0Ov1LF68mJs3b1KvXj21TFqiRAlThyfEU0WSNSGEEI8sKytLLZOuX78erVZLz5490el0dOrUCWtra1OHKITFk2RNCCFEobh+/TqLFy8mJCSEkydPUrp0aYYPH45Op6NatWqmDk8IiyXJmhBCiEKlKIpRmfTWrVs0aNAAnU7HoEGDntgSPUI8LSRZE0IIUWSysrJYv369Wia1srKiV69e6HQ6OnbsKGVSIQpAkjUhhBBPxLVr19Qy6alTp/D09FTLpFWrVjV1eEKYLUnWhBBCPFGKonDs2DG1THr79m0aNmyolknd3d1NHaIQZkWSNSGEECaTmZnJunXr0Ov1bNy4EWtra7VM2qFDBymTCoEka0IIIcxEfHy8WiY9ffo0ZcqUYfjw4QQFBREYGGjq8IQwGUnWhBBCmBVFUTh69CghISEsWbKEhIQEGjVqhE6nY+DAgVImFc8cSdaEEEKYrczMTNauXauWSW1sbOjduzfBwcG0b98eKysrU4coRJGTZE0IIYRFiIuLY9GiRej1esLDwylTpgwjRoxAp9NRuXJlU4cnRJGRZE0IIYRFURSFw4cPo9frWbJkCYmJiTRp0gSdTseAAQNwc3MzdYhCFCpJ1oQQQlisjIwM1q5dS0hICJs3b8bW1pbnnnsOnU5Hu3btpEwqngqSrAkhhHgqXL16lUWLFhESEkJkZCTlypVjxIgRBAUFERAQYOrwhHhkkqwJIYR4qiiKwqFDh9Dr9SxdupTExESaNm2qlkldXV1NHaIQD0WSNSGEEE+tjIwMVq9ejV6vZ8uWLdja2tKnTx+Cg4Np06aNlEmFRZBkTQghxDPhypUrapn0zJkzlC9fXi2T+vv7mzo8IR5IkjUhhBDPFEVR+Pvvv9Hr9Sxbtow7d+7QrFkztUxarFgxU4cohBFJ1oQQQjyz0tPTjcqk9vb29O3bF51OR5s2bdBqtaYOUQhJ1oQQQgiAy5cvs3DhQvR6PVFRUXh7e6tlUj8/P1OHJ55hkqwJIYQQ91AUhQMHDqhl0qSkJJo3b05wcDD9+/fHxcXF1CGKZ4wka0IIIcQDpKens2rVKkJCQti2bRsODg5qmbR169ZSJhVPhCRrQgghRAFcunRJLZOePXsWb29vgoKCCAoKolKlSqYOTzzFJFkTQgghHoKiKOzfv18tkyYnJ9OyZUt0Oh39+vWTMqkodJKsCSGEEI8oLS2NP//8E71ez/bt23FwcKBfv37odDpatWolZVJRKCRZE0IIIQpBbGysWiY9d+4cPj4+apnU19fX1OEJCybJmhBCCFGIFEVh79696PV6VqxYQXJyMq1atVLLpM7OzqYOUVgYSdaEEEKIIpKammpUJnVycqJ///7odDpatGghZVJRIJKsCSGEEE/AxYsXWbBgAXq9nvPnz+Pr60tQUBAjRoyQMqn4V5KsCSGEEE+QoiiEhoaqZdKUlBTatGmDTqejb9++ODk5mTpEYWYkWRNCCCFMJDU1lT/++AO9Xs/OnTtxdnY2KpNqNBpThyjMgCRrQgghhBmIiYlRy6QXLlygYsWK6HQ6RowYQYUKFUwdnjAhSdaEEEIIM2IwGAgNDSUkJITffvuN1NRU2rZti06no0+fPlImfQYV6m0oOp2O3r17P1YbMTExaDQawsLCCiUmS6DX63FzczN1GEIIIcyAVqulZcuWhISEEB8fj16vx2AwMGLECDw9PRk1ahShoaHIWMuzw+zvGTZl8ubj48PMmTMLvL8kXUIIIQqTs7MzQUFB7Ny5k/PnzzN27Fh27NhBixYt8Pf354svviA2NtbUYYoiZvbJWmFSFIXUbAOJmbnczsglMTOX1GyDfDsRQghh9nx9fZkwYQLR0dHs3LmT5s2bM3nyZHx8fGjfvj2LFi0iLS3N1GEWiWe9/36oZO3kyZO0bdsWBwcHSpQowQsvvEBKSkq+/SZOnEipUqUoVqwYL774IllZWepzmzZtonnz5ri5uVGiRAm6d+9OdHR0gWPYtWsXGo2G7du3U79+fRwdHWnatClnzpxR9/n000+pXbs23/84G6+y5bB3cKRRl95MCj3P96du81N4AvWbt6Rb8Bi+P3WbGSduseRsIs079+C5IcNJyzHQunVrLl68yFtvvYVGo/nPO3J27dpFcHAwd+7cUff/9NNPAUhISGDEiBG4u7vj6OhIly5dOHv27APbunXrFg0bNqRnz55kZGSgKApfffUVFStWxMHBgVq1avH7778/1Dk5fvw4bdq0wcXFhWLFilGvXj0OHz5c4PMuhBDCPGi1Wlq3bo1eryc+Pp558+aRk5PD8OHD8fT0ZPTo0ezdu9eiE5m0HAMRCZnsvJLKkrOJzDhxS+2/f45I4KfwBKP+e+eVVCISMknLMZg69CJR4GQtLS2Nzp074+7uzqFDh/jtt9/Ytm0br776qtF+27dvJyIigp07d7J06VL+/PNPJk6cqD6fmprK22+/zaFDh9i+fTtarZbnnnsOg+HhTvBHH33E9OnTOXz4MNbW1owcORK4m30nZeVy5uw5vtUvpf/0hei+X0b06RP8Nvm9+7aVZYDYlBwSMnOJTclm1snbvDBTj1fZckycOJG4uDji4uL+NZ6mTZsyc+ZMihUrpu4/duxY4O61fIcPH2bNmjXs378fRVHo2rUr2dnZ+dq5fPkyLVq0oEqVKqxcuRJ7e3s+/vhjQkJCmD17NqdPn+att95i2LBh7N69u0DnBGDo0KGUK1eOQ4cOceTIEd5//31sbGwe6pwLIYQwLy4uLuh0Onbt2sW5c+d466232Lp1K82bNycgIIAvv/ySS5cumTrMAlEUhSup2ayNSWLWydusjknm0PV0YlNyyHpAipDXfx+6ns7qmGRmnbzN2pgkrqRmW3Sy+k/WBd1x8eLFpKens2DBAvVOlFmzZtGjRw+mTp1K6dKlAbC1tWXevHk4OjpSrVo1PvvsM8aNG8fnn3+OVqulb9++Ru3OnTuXUqVKER4eTvXq1Qsc+JdffkmrVq0AeP/99+nWrRsn4+/wd4KBE7cyycrMoP9ns3AtXQaAHu9OZv4bQ+j21kRcSpb+17YNwCXFmXSDhvB0a5Id3PF3tfvX19ja2uLq6opGo8HT01PdfvbsWdasWcPevXtp2rSpei7Lly/PqlWr6N+/v7pvVFQUHTp0oFevXnz77bdoNBpSU1P55ptv2LFjB02aNAGgYsWKhIaGMmfOHPUcPOicZGRkYG9vT2xsLOPGjaNKlSoA+Pv7F+Q0CyGEsBCVKlVi4sSJTJgwgd27d6PX65k0aRLjx4+nffv26k2Ajo6Opg41n6jETP6KS+NmRi4aIC/NKugwjuGe/4YnZHE6IQsPeytalnH8z/7bEhR4ZC0iIoJatWoZ3TLcrFkzDAaDUbmtVq1aRr8ITZo0ISUlRc3so6OjGTJkCBUrVqRYsWLqEhsPe4FkzZo11X+7e9xNvhYfvcCtjFwAXD3LqYkaQIWaDVAMBm5cPFeg9vN+UVKzDfxxPpnVMUmkP8LwakREBNbW1jRq1EjdVqJECSpXrkxERIS6LT09nebNm9O7d2++++47tewaHh5ORkYGHTp0wNnZWX0sWLAgX/n43nPi5eUFwPXr1wF4++23ef7552nfvj1Tpkx5qNKzEEIIy6HVamnTpg3z588nPj6eX3/9lYyMDIYOHYqXlxcvvPAC+/btM4uRp/QcA6tjklh5IVntvx83qrzX38zIfaz+25wUOFlTFOWB120VZIblvH169OjBrVu3+OWXX/j777/5+++/AYyuayuIvBJeVGImKy8k/y9Gw4M/5P8dX8P//qvR8s9fidyc/GXJvD0iE7L4OTyBqMTMh4rzQX8M/zyfdnZ2tG/fnvXr13P58mV1e155eP369YSFhamP8PBwo+vWAKOyZl7bea//9NNPOX36NN26dWPHjh1UrVqVP//886HeixBCCMvi4uLCyJEj+euvvzh79ixvvPEGmzdvplmzZlSpUoXJkycb9TlPUlRiJj+HJxCZcLf/L+zU8XH7b3NS4GStatWqhIWFkZqaqm7bu3cvWq2WgIAAddvx48dJT09Xfz5w4ADOzs6UK1eOW7duERERwccff0y7du0IDAwkISHhkQJXFIV98WmsvJBMpiH/R3wn/jJJN+LVn2NPHEKj1VKyQiUAnNxLkHzzmvq8ITeXa+cijdqwsrFB+V+yowDpuQorLySzPz7tvkmYra0tubm5RtuqVq1KTk6OmpTC3RsIoqKiCAwMVLdptVoWLlxIvXr1aNu2LVevXlVfb2dnR2xsLH5+fkaP8uXLF/R0ARAQEMBbb73Fli1b6NOnDyEhIQ/1eiGEEJbLz8+Pzz77jAsXLrB9+3YaNWrE559/jre3N506dWLp0qVG/XdRubf/Ts9VCj1Jy3c8/rv/NncFTtaGDh2Kvb09QUFBnDp1ip07d/Laa68xfPhw9Xo1uDtCNmrUKMLDw9m4cSMTJkzg1VdfRavV4u7uTokSJfj55585d+4cO3bs4O23336kwPfGp/FX3INvUba2teO3T14lLuoUF47uZ+3XH1KjQy/1erVKDVoQuWcbkXu2cP3CWVZPfpf0lDtGbbiX8ebC0f3cuR5HasItdfvuuLvH/ucH7uPjQ0pKCtu3b+fmzZukpaXh7+9Pr169GD16NKGhoRw/fpxhw4ZRtmxZevXqZfR6KysrFi9eTK1atWjbti3x8fG4uLgwduxY3nrrLebPn090dDTHjh3jhx9+YP78+QU6V+np6bz66qvs2rWLixcvsnfvXg4dOmSULAohhHg2aLVa2rZty4IFC4iPj+eXX34hLS2NIUOG4OXlxUsvvcSBAweKJKlRFEXtQ03hQf23uStwsubo6MjmzZu5ffs2DRo0oF+/frRr145Zs2YZ7deuXTv8/f1p2bIlAwYMoEePHuoUFlqtlmXLlnHkyBGqV6/OW2+9xddff/1IgR+6kfGvz5co70u1tt3QvzaYea8MoHSlKvT+4Cv1+fq9hlC3x0BWfPIqv4zuhXtZbyrVb27URoeX3iPh6iWm9WzAF+2qGD23/1o6B64ZfwNp2rQpL730EgMHDsTDw4Ovvrp7vJCQEOrVq0f37t1p0qQJiqKwYcOG+96NaW1tzdKlS6lWrRpt27bl+vXrfP7553zyySdMnjyZwMBAOnXqxNq1a9Xr/f6LlZUVt27dYsSIEQQEBDBgwAC6dOlidJeuEEKIZ0+xYsUYNWoUe/bsISoqildffZX169fTpEkTAgMDmTJlCleuXCm0492v73zSzCGGh2Vxa4Pee43ag2z76SvCd23g9WW7ijyePr4uBLhZ/p0mQgghBEBubi47d+5Er9fzxx9/kJWVRYcOHQgODqZXr17Y29vne01ISAitW7f+10GEgvTfT5Il9d8WtYJBeo6BjbH5J+E1pY2xKRZ/l4kQQgiRx8rKSl0RIT4+njlz5pCcnMygQYPw8vLi5Zdf5u+//1ZLiWFhYYwcOZIWLVo88GYF6b8fj0WNrK2OSSIyIes/L0YsqpG1kFcHEnPsQL7tWo2G8R99yIcfflioxxNCCCHMRVRUFPPnz2fBggVcvnyZKlWqoNPpOHfunHrDWsWKFdm3bx8lS5Y0em1B++8nSQMEutvS06eYqUP5TxaTrJnD8Omd63FkZ9y/zj2wVnka+Hg94YiEEEKIJys3N5cdO3aoZdLMzP+fEsPKyoqaNWuye/duXFxcAPPov/9N34ouZj9xrkUka4qiMDcykVsZuWaVlefRACXtrRhZxa1Ac84JIYQQT4MlS5YwdOhQo20ajYY6deoQGhqKvb299N+FwCKuWbualsNNM/2g4e4cLjcycrmalmPqUIQQQognZvny5UZJjkajQVEUjh49Sv/+/aX/LiQFXhvUlI7eSDdaK8wcabgbZ1knWRxdCCHEs+HKlSvY2tpSunRpvL298fb2xsvLi4yMDEaNGiX9dyEx+2QtLcdAhJldlHg/ChCRkEW7cgYcrS1iwFIIIYR4LAcPHkSj0dy3hJiWY2DrydvSfxcC84zqHheTs7GMG2vBAMQm519fVAghhHgaabXaB17rJf134TH7ZC0+Lcf8g/wfLXfjFUIIIZ510n8XHrM/j3FplpWZm/tFikIIIcSTIP134THrZE1RFLPOdO8nPi3H4haIFUIIIQqT9N+Fy6yTtbQchSxLScv/J8ugkJZjnh+2EEII8SRI/124zDpZyzaY50n7LzlmmpkLIYQQT4L034XLrJM1C/2sybWwbxNCCCFEYZL+u3CZdbKmNd+VH/6VlVmfVSGEEKJoSf9duMw0rLtsLPTTtjbj9cWEEEKIoib9d+Ey62TN0VqDrVlHmJ+Sncnq35cTERFBbm6uqcMRQgghnjhL7L9ttRocrc0zWTPr5aY0Gg2ejtbEpljG7b+KonAl4gQf6oYA4ODgQM2aNaldu7b6qFGjBk5OTiaOVAghhCg6ltZ/A3g6Wj9wNQZT0yjmOqnI/+y8ksqh6+kWMbGeFmhYyoGaDpkcP36csLAw9REeHk5OTg4ajYaAgACjBK527dp4enqaOnwhhBCi0Fhi/926rHkOppj1yBrczXQt4YOGuzMgezpaU9zdiTZt2tCmTRv1uczMTMLDw9Xk7dixY2zYsIHk5GQASpcuTZ06dYwSOD8/P6ysrEz0boQQQohHZ4n9t7ky+5G1tBwDs07etogPXAu8WqM4jtYFK9QbDAZiYmKMRuDCwsK4dOkSAI6Ojvctozo6OhbhuxBCCCEe39Pcfz9pZp+sAayNSSI8IQtzDlQDVHW3pYdPscdu69atW2oZ9dixY4SFhak3LGi1WrWMeu9IXKlSpR7/TQghhBCF6Fnrv4uKRSRrV1KzWRh1x9Rh/KfhAa6UdbIpkrYzMjI4ffq00Qjc8ePH1TKql5dXvuvg/Pz80GrN81uCEEKIp5/034XDIpI1RVGYF5nIzYxcs8zONUBJeytGVnF7oneSGAwGLly4YJTAHTt2jCtXrgDg5OR03zKqg4PDE4tRCCHEs0v678JhEckawNk7mfxxPtnUYTxQ34ou+LvamToMAG7cuJHvbtTIyEi1jFqlSpV8o3AeHh6mDlsIIcRTSPrvx2cxyRrA6pgkIs2s9q0BAt1t6WnGtW6A9PT0+5ZRU1JSAChTpky+BK5SpUpSRhVCCPHYpP9+PBaVrKXnGPg5PIH0XPMJ2cFKwwtV3XEw0ztI/o3BYCA6Ojrf3ahXr14FwNnZmVq1ahklcNWqVZMyqhBCiP+k0+lITExk1apVav99cPNqVnw8hvYvvUcr3Wuk3Ulg+8/TCN+1keQb8Ti6uRPQpC3tX3oPN69yAMx/YyjZmRk8/9Mf+Y5x8fghfgruyquLt1E2sFaB4po7pj/RB//ijfkbmDSog0X03+Y7qch9OFhr6eLtzMoL5jOc2sXb2SI+6PvRarX4+/vj7+9P//791e3Xr183KqPu3LmT2bNnYzAYsLKyum8ZtWTJkiZ8J0IIIcyZg7WWlF0rWP7Rm/R6fwoNnhtO2p0EfgzqjLWNLb0+mIpnpUASrsay5cfJ/DC8Iy/rN1C8nA/1ew9l8VgdCVcv4V6mvFG7R9Yswaty9QInaolxl4k9cYgmA0dxZetyHIZ1Koq3W+gsamQtz774NP6KSzN1GLTycqSJ57Mx51laWhqnTp3KV0ZNS7v7OZQtWzbfdCK+vr5SRhVCiGfUvSNrX331FZ988gnjZ83Dql5HAFZNGsex9SsYu/ogLiVLq6/LzkhnWu9GePoFEjxrObk5OUzpUpNG/YJp/+I4db+s9DQmdaxGx1c+oumg5wsU07Y5X3Mj5izvfzSeF7u1Ii4uziKWgLSokbU8TUo7kG1Q2H8t3aQxNC797JQDHR0dadiwIQ0bNlS35ebm5iujzp07l7i4OABcXFzuW0a1t7c31dsQQgjxhL3//vv88MMPrFu3jnbt2vFXXBp741I5vvlPanfpZ5SoAdjYO9C4fzBbf5xM2p0EHF3dqdttIEfXLqPdC2PVuzZPbltDbnY2dbr2K1AciqJwZM1S3psyg0HNazM9IIAVK1YQHBxc6O+5sFlksqbRaGjp5YitVsNuE4ywPUsjav/GysqKgIAAAgICGDBggLr92rVrRgnctm3b+OGHH1AUBSsrKwIDA9XkrU6dOtSqVYsSJUqY8J0IIYQoChs3bmT16tVs376dtm3bAtDSy5GUW9fJSL6Dh6//fV9XyjcARVG4dekCjq7u1Os1hL8WzOL84b1UatAcgCOrl1CtbTccirkVKJZzf++GrHTGDumFRqNh2LBhzJ07V5K1oqTRaGji6UgJeys2xqaQkasU6V0mGsDeSkMXb2cC3Mz7Fl9TK126NJ06daJTp/+/FiA1NTVfGXXlypVqGbV8+fL5roPz9fU163lvhBBC/LuaNWty8+ZNPvnkExo0aICLiwsajYb6pe4OeNhoNWggX/+dd4WWlc3diWpL+fpToVYDDq9eQqUGzbl16QIxxw4w8sff/jOGvP47fusKhg0ehLX13dRn8ODBjBs3jjNnzlC5cuXCestFwmKTtTwBbnaUd7Zhy+UUIhKy7vuhP4689gLdbelQznJvJjA1JycnGjVqRKNGjdRtubm5nDt3zmhZrTlz5nD9+nUAihUrdt8yqp2dJMtCCGEJypYtyx9//EGbNm3o3LkzmzZtwsXFBQ8PD9zc3HC/HUMVd9t8/feNmLNora0pXqaC2lb9XkNZ89UHZKRM5ciapbh5ladSw5YPPPa9/Xc9xyze27CGTdnZzJ49W90nNzeXefPmMXXq1CJ5/4XFIm8weJCoxEz2xKVxIyP3sZO2vNd72FvRsoyj2U+Y9zSJj4/PN51IVFQUiqJgbW1N1apVjRK4WrVqUbx4cVOHLYQQ4h733mBw+fJl2rRpQ8mSJdm8eTPFihXjxRdfZPHixZw7d44ke3e1/87JSOfr3o2oUKsBQ6bOVdvLTEthUsfqdH3zU3bOnUGD54bT7oWx+Y57v/77+++/59tvv2XVqlVG+27fvp3Jkydz+fJldcTNHD1VyRrcHTq9mpbD0RvpRCRkYQC0gKEAr83bT6uBqm521PWwx8vRWkpxZiA1NZWTJ08aLat14sQJMjIyAPD29s5XRvXx8ZHPTgghTOTeZA3gypUrtGnThuLFi7N582ays7Np3LgxDg4OfPXVV1SrVo1DEef4ZPx4rly6yJj5m3Dz8DTqv//47E1O71hPRkoS7649os7F9l/9d+3atencuTNTpkwxijE5ORkPDw+WL19Or169iv6kPKKnLlm7V1qOgdjkbOLScohLyyE+LZus+2RttlrwdLShjKM1no7WeLvY4CjlTrOXk5PD2bNn862NeuPGDQBcXV3zJXBVq1bF1tbWxJELIcTT75/JGkBcXBxt2rShWLFibNmyhZycHD777DNWrVrF1atXyc3NpWnTpiz/czUpNi75+u+8SXD9G7dm5I+/Faj/PnLkCPXr1+fgwYM0aNAgX5w9e/YEYM2aNUV6Ph7HU52s/ZOiKKTlKOQoCrkGsNKCtUaDo7VGRmCeEoqi3LeMevbsWRRFwcbG5r5lVHd3d1OHLoQQz7y5c+cyZswYli9fTu/evdXtz3r//Uwla+LZlZycbFRGDQsL4+TJk2oZtUKFCkbTidSuXRtvb+9n4n8CQghhTv78808iIyN58803ZXnD/5FkTTyzcnJyiIqKyldGvXnzJgBubm75yqiBgYFSRhVCCDPz0ksvsWjRovs+N2zYMH766acnHFHhkmRNiHsoisLVq1fzlVHPnTsHgI2NDdWqVcuXxLm6upo4ciGEeHZdv36dpKSk+z5XrFgxSpUq9YQjKlySrAlRAMnJyZw4cSJfGTUzMxMAX1/ffAlc+fLlpYwqhBDisUmyJsQjysnJ4cyZM+qEvnmPW7duAeDu7n7fMqrN/2bkFkIIIQpCkjUhCpGiKFy5ciVfGTU6OhoAW1tbqlevbpTA1axZU8qoQgghHkiSNSGegKSkJLWMmjcSd+rUKbKysgCoWLFivlG4cuXKSRlVCCGEJGtCmEp2djaRkZH57kZNSEgAoHjx4kZTidSuXZvKlStLGVUIIZ4xkqwJYUYUReHy5ctGyVtYWBgXLlwAwM7O7r5l1GLFipk4ciGEEEVFkjUhLEBiYmK+u1FPnTpFdnY2AJUqVco3qW+ZMmWkjCqEEE8BSdaEsFBZWVlGZdS8UbjExEQASpYsme86uMqVK2NtbW3awIUQQjwUSdaEeIooikJsbGy+u1FjYmKAu2XUGjVq5Cujuri4mDZwIYQQDyTJmhDPgISEhHxl1NOnT5OdnY1Go8HPzy/fKJyXl5eUUYUQwgxIsibEMyorK4vw8PB8o3B37twBwMPDI18CFxAQIGVUIYR4wiRZE0KoFEXh4sWL+RK4ixcvAmBvb0+NGjWMphOpUaMGzs7OJo5cCCGeXpKsCSH+0+3btzl+/LhRAhceHk5OTg4ajQZ/f/98o3Cenp5SRhVCiEIgyZoQ4pFkZmbet4yalJQEQKlSpfJNJ+Lv74+VlZWJIxdCCMsiyZoQotAoikJMTEy+xe0vXboEgIODAzVr1jQagatRowZOTk4mjlwIIcyXJGtCiCJ369at+5ZRc3Nz0Wg0BAQEGCVwderUoXTp0qYOWwghzIIka0IIk8jIyCA8PNxoFO748eMkJycD4Onpme86OD8/PymjCiGeOZKsCSHMhsFg4MKFC/mug7t8+TIAjo6O9y2jOjo6mjhyIYQoOpKsCSHM3s2bN9Uyat5IXGRkJLm5uWi1WipXrpxvFK5UqVKmDlsIIQqFJGtCCIuUnp7O6dOnjUbgjh8/TkpKCgBeXl73LaNqtVoTRy6EEA9HkjUhxFPDYDAQHR2d72aGK1euAODk5ETNmjWNJvWtXr06Dg4OJo5cCCEeTJI1IcRT7/r16/kSuMjISAwGA1qtlipVquQbhfPw8DB12EIIAUiyJoR4RqWnp3Pq1Kl8ZdTU1FQAypQpk29S34oVK0oZVQjxxEmyJoQQ/5NXRv3npL5xcXEAODs7U6tWLaMRuOrVq2Nvb2/iyIUQTzNJ1oQQ4j9cu3YtXxn1zJkzGAwGrKys7ltGLVmypKnDFkI8JSRZE0KIR5CWlsbJkyeNErgTJ06QlpYGQLly5fIlcL6+vlJGFUI8NEnWhBCikOTm5nLu3Ll8k/rGx8cD4OLikq+MWq1aNSmjCiH+lSRrQghRxOLj4zl+/LjRtXBRUVEoioKVlRWBgYFG04nUqlWLEiVKmDpsIYSZkGRNCCFMIDU19b5l1PT0dADKly9/3zKqRqMxceRCiCdNkjUhhDATubm5nD17Vk3ejh07xrFjx7hx4wYAxYoVU8uoeSNxVatWxc7OzsSRCyGKkiRrQghhxhRFIT4+Pt91cGfPnkVRFKytralatarRCFytWrUoXry4qUMXQhQSSdaEEMICpaSk3LeMmpGRAYC3t7fRCFzt2rWpUKGClFGFsECSrAkhxFMiJyeHqKgoowTu2LFj3Lx5EwBXV9d818FVrVoVW1tbE0cuhPg3kqwJIcRTTFEU4uLi7ltGBbCxsTEqo9apU4datWrh5uZm2sCFECpJ1oQQ4hmUnJzMiRMnjBK4kydPkpmZCYCPj0++UThvb28powphApKsCSGEAO6WUc+cOZOvjHrr1i0A3Nzc8iVwgYGBUkYVoohJsiaEEOKBFEXh6tWrauKWl8RFR0cDYGtrS7Vq1fLdjerq6mriyIV4ekiyJoQQ4qElJSXdt4yalZUFgK+vb75RuPLly0sZVYhHIMmaEEKIQpGdnX3fMurt27cBKF68eL4ErkqVKtjY2Jg4ciHMmyRrQgghioyiKFy+fDnf3ajnz58H7pZRq1evnq+MWqxYMRNHLoT5kGRNCCHEE3fnzh2jMuqxY8c4ffq0WkatWLGi0XQitWvXpmzZslJGFc8kSdaEEEKYhaysLCIjI/ONwiUkJABQokSJfGXUypUrSxlVPPUkWRNCCGG2FEXh0qVL+RK4CxcuAGBnZ3ffMqqLi4uJIxei8EiyJoQQwuIkJiZy/PhxowTu9OnTZGdnA+Dn55dvFK5MmTJSRrVQiqKQlqOQbVAwKKDVgI1Wg6O15pn4TCVZE0II8VTIysoiIiIi3yhcYmIiACVLljRK3urUqUNAQADW1tamDVzkk5Zj4GJyNvFpOcSl3f1vliH/frZa8HS0xsvRBk9Hayq42OBorX3yARcxSdaEEEI8tRRFITY21mhC37CwMC5evAiAvb09NWrUMEriatasibOzs4kjf/YoisLVtByO3kgnIiELA6AF7pOj5ZO3nxYIdLelrocDZRytn5pRN0nWhBBCPHMSEhLuW0bNyclBo9Hct4zq5eX11HT+5iYqMZO/4tK4mZGLBnicxCTv9R72VrQs44i/q13hBGlCkqwJIYQQQGZmplpGvXckLikpCYBSpUrlS+ACAgKwsrIyceSWKz3HwJbLKUQkZD12kvZPee0FutvSsZwzDhZcHpVkTQghhHgARVGIiYnJdx1cbGwsAA4ODvnKqDVq1JAyagFEJWayMTaFjFylUJO0f9IA9lYaung7E+BmmaNskqwJIYQQD+n27dtGZdRjx44RERGhllH9/f3VyXzzHp6enqYO2ywoisL+a+n8FZf2xI/dysuRxqUdLK6cLcmaEEIIUQgyMjIIDw/PNwqXnJwMQOnSpfOVUf39/Z+pMqqiKOyOS+PAtXSTxdCktAMtvRwtKmGTZE0IIYQoIgaD4b5l1EuXLgF3y6g1a9Y0WlarRo0aODo6mjjyorEvPs0kI2r/1MrLkSaelnOOJVkTQgghnrCbN2/muxs1IiKC3NxctFotAQEB+UbhSpcubeqwH0tUYiYrLySbOgxVH18Xi7mGTZI1IYQQwgxkZGRw+vTpfKNwKSkpAHh6euab1LdSpUoWUUZNzzHwc3gC6bnmk3I4WGl4oaq7RdwlKsmaEEIIYaYMBgPnz5/Pl8BduXIFACcnJ7WMmveoXr262ZVRV8ckEZmQVaR3fT4sDXen9ejpU8zUofwnSdaEEEIIC3Pjxo37llENBgNarZbKlSvnK6OWKlXKJLGaW/nzn/pWdDH7iXMlWRNCCCGeAunp6Zw6dcoogTt+/DipqakAlClTJl8CV6lSJbTaoisDKorC3MhEbmXkmtWoWh4NUNLeipFV3Mz67lBJ1oQQQoinlMFgIDo6Ol8Z9erVq8DdMmqtWrXylVEdHBwK1P6cOXOwt7dnxIgR9012rqRmszDqTqG+p6IwPMCVsk42pg7jgSRZE0IIIZ4x169fV8uoeUtrnTlzRi2jVqlSJd+kviVLljRqQ1EU3NzcSEpKYsiQIcyZMyffyg1rY5IIN7Nr1f5JA1R1t6WHGV+7JsmaEEIIIUhLS7tvGTUt7e68aGXLls03lUjLli0B0Gq1VKpUiVWrVlG1atW77eUYmHXyNgaTvaOC0wKv1iiOo5neGSrJmhBCCCHuKzc3N18Z9dixY8THx+fbV6vVYm1tzezZsxk5ciQRCZmsjjHfGwv+qbePC1XczfNGA0nWhBBCCPFQ4uPjeffdd1m8eDEGQ/6xs3HjxtHljQkcup5uMSNrDUs50Lqsk6lDuS9rUwcghBBCCMvi6elJSkqKeo0b3L2ZwdPTk2rVqjF06FBOp2VbRKIGYACupuWYOowHkmRNCCGEEA/txo0b2Nra0qZNG7p160aXLl3w8/MD7t58sP3ELRNH+HDi03JQFMUsp/CQZE0IIYQQD23r1q0oinLfaT7SchSyLGVY7X+yDAppOQpONpKsCSGEEOIpYG9v/8Dnsg2WeTl8jplexm+e96gKIYQQwmJZaK5GrpmOBkqyJoQQQohCpTW/SmKBWJlpVmSmYQkhhBDCUtlYaLZmbYY3F4Aka0IIIYQoZI7WGmwtLMOw1WpwtJZkTQghhBDPAI1Gg6ejZd3D6OlobZbTdoAka0IIIYQoAl6ONhaTZGiBMmacXFrKeRRCCCGEBfF0tLaoFQzMeSRQkjUhhBBCFLoKLpY1subtYmPqMB7IUs6jEEIIISyIo7WWQHdbzPMqsP+nAQLdbXG0Nt+UyHwjE0IIIYRFq+vhgLnPj6twN05zJsmaEEIIIYpEGUdrPOytzHZ0TQN42FuZ9c0FIMmaEEIIIYqIRqOhZRlHsx1dU4CWZRzNdsqOPJKsCSGEEOKxffrpp9SuXTvfdn9Xu8e6di3haiwf1PXg6pmTD9zn/OG9fFDXg/TkOwAcWbOUiS0r/Wu7GqCquy3+rnaPGNmTI8maEEIIYQbi4+N544038PPzw97entKlS9O8eXN++ukn0tLSCu04Op0OjUaT79G5c+dCO8Y/dSznjL3Vkxu9qtmxN++sOvCv+9hbaehQzrnQjunj48PMmTMLrb17mXeRVgghhHgGnD9/nmbNmuHm5sakSZOoUaMGOTk5REVFMW/ePMqUKUPPnj0L7XidO3cmJCTEaJudXdGNMDlYa+ni7czKC8lFdox72dg7YGP/7zcNdPF2xsGM7wC9l2VEKYQQQjzFxowZg7W1NYcPH2bAgAEEBgZSo0YN+vbty/r16+nRowcAd+7c4YUXXqBUqVIUK1aMtm3bcvz4cbWdvFLkwoUL8fHxwdXVlUGDBpGcbJwk2dnZ4enpafRwd3dXn9doNMyZM4fu3bvj6OhIYGAg+/fv59y5c7Ru3RonJyeaNGlCdHR0vvcyZ84cypcvj6OjI/379ycxMRGAADc7Wno5cnj1Er7p05TxjcvxTZ8m7F8xz+j1l04d5bvBbRjfuByzhrbnamT+8mdk6Fam9W7E+Cbl+eWF3iRcjTV6/p9l0G0/fcV3g1pzdN0KpnaryxetKvHJS0FG5yU5OZmhQ4fi5OSEl5cXM2bMoHXr1rz55pv/8elB69atuXjxIm+99ZY6Upnnjz/+oFq1atjZ2eHj48P06dP/s71/kmRNCCGEMKFbt26xZcsWXnnlFZycnO67j0ajQVEUunXrRnx8PBs2bODIkSPUrVuXdu3acfv2bXXf6OhoVq1axbp161i3bh27d+9mypQpDx3X559/zogRIwgLC6NKlSoMGTKEF198kQ8++IDDhw8D8Oqrrxq95ty5c6xYsYK1a9eyadMmwsLCeOWVV9TnT61ZxK6fJtPxlQ9564+9dHzlI7bOnsKRtcsAyEpPZf4bQ/Go4Meri7fR7sV32TDzU6NjJMZfYfHYYCo3a8/rS3dSv/cwNn3/+X++n1uXYwjftYFvFv3OhnVr852Xt99+m71797JmzRq2bt3Knj17OHr0aIHO1cqVKylXrhyfffYZcXFxxMXFAXDkyBEGDBjAoEGDOHnyJJ9++injx49Hr9cXqF2VIoQQQgiTOXDggAIoK1euNNpeokQJxcnJSXFyclLeffddZfv27UqxYsWUjIwMo/0qVaqkzJkzR1EURZkwYYLi6OioJCUlqc+PGzdOadSokfpzUFCQYmVlpbad9/jss8/UfQDl448/Vn/ev3+/Aihz585Vty1dulSxt7dXf54wYYJiZWWlXLp0Sd22ceNGRavVKnFxcYqiKEr58uWVxYsXK/viUpXJR28ok4/eUDqMeV/xrtlAmXz0hvLcR9MVB1d3ZeLei+rzvT/8WgGU15buUCYfvaG0Dn5DKeUboEw6cl3dp5XuNQVQPtl9Tpl89IbS79PvFHvnYurz7V4Yp9jYOypbz8bf97wkJSUpNjY2ym+//aY+n5iYqDg6OipvvPFGAT5FRalQoYIyY8YMo21DhgxROnToYLRt3LhxStWqVQvUZh65Zk0IIYQwA/+cPuLgwYMYDAaGDh1KZmYmR44cISUlhRIlShjtl56eblSO9PHxwcXFRf3Zy8uL69evG72mTZs2zJ4922hb8eLFjX6uWbOm+u/SpUsDUKNGDaNtGRkZJCUlUaxYMQC8vb0pV66cuk+TJk0wGAycOXMGKysrLl26xPPPP49W+wIKkGMAQ24O9s53X3/9QhRe/tWwdXBU2/CuWd8orusxZylfo57R+fKu2YAH0QDWWqjgU4H2fqXve17Onz9PdnY2DRs2VJ93dXWlcuXKD2y3ICIiIujVq5fRtmbNmjFz5kxyc3OxsrIqUDuSrAkhhBAm5Ofnh0ajITIy0mh7xYoVAXBwuHuhvMFgwMvLi127duVrw83NTf23jY3xGpcajQaDwXhJdScnJ/z8/P41rnvbyUuM7rftn23/89j/jOGXX36hUaNGAGTkGAiNT+V8igENoCgFmJGtIPvcI9DdliQPB+LtbPPFlhdT3nH/mTAXKJ5/oShKobQp16wJIYQQJlSiRAk6dOjArFmzSE1NfeB+devWJT4+Hmtra/z8/IweJUuWfIIRP1hsbCxXr15Vf96/fz9arZaAgABKly5N2bJlOX/+vBp39SoBvNS6Ds83r05JeytKV6xM/NnTZGek/3+bJ44YHaOUbwCXThpviz15WP13Xmqk0UDfii709CmGjfbfpw2pVKkSNjY2HDx4UN2WlJTE2bNnC/zebW1tyc3NNdpWtWpVQkNDjbbt27ePgICAAo+qgSRrQgghhMn9+OOP5OTkUL9+fZYvX05ERARnzpxh0aJFREZGYmVlRfv27WnSpAm9e/dm8+bNxMTEsG/fPj7++GP1gv+CyszMJD4+3uhx8+bNx34f9vb2BAUFcfz4cfbs2cPrr7/OgAED8PT0BO7erTp58mS+/fZboqKiOHnyJCEhIayb9wMjq7gx5bVgrK20rPzsTa6dP0NU6Fb2LPzB6BiN+um4dTmGddPHcyPmHGEb/+Do/25Q0GqgmrsdTT0dsdVqCjzhrYuLC0FBQYwbN46dO3dy+vRpRo4ciVarLfDqBv/X3h275HHHcRz/XptaaSk0YChiB4MiCAYHJRAMsRQREpwySUshJOnQWSm4CH1cCtUMTSoUhOomLl1aSYYsPn+AHU2g4GL3QFKXpE8HaSCUtIZU+hleL7jp7rj73fTm7n53/f39tbOzUwcHB8+v5dzcXN2/f7+Wlpbq4cOHtbGxUXfu3Kn5+fnjXtKjcb3S1gDAf25gYKB2d3dramqqFhYWanR0tMbHx+v27ds1Pz9fS0tL1TRNbW9v16VLl+r69es1NDRUs7Oztb+///ydsuO6e/du9fb2vrBcvHjxtccxODhYV69erStXrtT09HSNjIzU6urq8/U3b96stbW1Wl9fr3PnztXk5GStr6/X2bNnq2maGvrgdN37+ad69tuv9d0nH1f7+6/rsy+/euEY7/d+WJ9+80Pt7dyrb2c/ql9+3KgvFo62+Xz4dM30v1en3z7+Xau/3Lp1qy5cuFAzMzM1NTVVExMTNTw8XN3d3cfav9Vq1f7+fg0MDNSZM2eq6uhu6NbWVm1ubtbIyEgtLi5Wq9Wqa9euvdK5NZ3XfSALAHCCOp1O/f60U087nXr2R9Wbb1Sdapp651RzYv/1fPLkSfX19dXKykrduHHjRI5xXCYYAADRmqapd9862d9V7e7u1t7eXp0/f74ePXpUrVarqupvszn/D2INAKCqlpeX68GDB9XV1VVjY2PVbrerp6en2u12Xb58+aX7PX78+ETPy2NQAIB/cHh4WAcHBy9d/2+fQXldYg0AIJjZoAAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABBMrAEABBNrAADBxBoAQDCxBgAQTKwBAAQTawAAwcQaAEAwsQYAEEysAQAEE2sAAMHEGgBAMLEGABDsT6xrQzpanQPZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x1900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total allocated: 2.7515182495117188 MB\n",
      "cumsum_input: tensor([0, 7])\n",
      "input_tensor: tensor([128000,  13347,     11,    889,    527,    499,     30],\n",
      "       device='cuda:0', dtype=torch.int32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GlobalInput_0', 'GenEmbedding_0', 'LayerNormAttn_0', 'KQV_0', 'RopeAppend_0', 'DecAttn_0', 'PFAttn_0', 'O_0', 'LayerNormFFN_0', 'UG_0', 'Activation_0', 'D_0', 'LayerNormAttn_1', 'KQV_1', 'RopeAppend_1', 'DecAttn_1', 'PFAttn_1', 'O_1', 'LayerNormFFN_1', 'UG_1', 'Activation_1', 'D_1', 'LayerNormAttn_2', 'KQV_2', 'RopeAppend_2', 'DecAttn_2', 'PFAttn_2', 'O_2', 'LayerNormFFN_2', 'UG_2', 'Activation_2', 'D_2', 'ModelLayerNorm_2', 'GetLogits_2', 'Sampling_2', 'GlobalOutput_2']\n",
      "GlobalInput_0\n",
      "GenEmbedding_0\n",
      "cuda:0\n",
      "tensor([[-8.2970e-05,  2.5749e-04, -2.4605e-04,  ..., -3.2425e-04,\n",
      "         -2.1553e-04,  4.7112e-04],\n",
      "        [ 1.3733e-03, -9.1553e-03,  1.9409e-02,  ...,  4.9744e-03,\n",
      "          1.8066e-02,  3.0060e-03],\n",
      "        [-2.5558e-04, -4.1199e-04,  3.6478e-05,  ...,  2.1267e-04,\n",
      "          3.1471e-05,  2.9182e-04],\n",
      "        ...,\n",
      "        [ 3.6621e-03,  7.0572e-04,  1.0071e-03,  ...,  3.0975e-03,\n",
      "         -1.8692e-04,  7.9346e-03],\n",
      "        [-6.9275e-03, -4.8523e-03, -2.3193e-03,  ..., -2.6398e-03,\n",
      "          1.1063e-03,  7.2861e-04],\n",
      "        [-5.0049e-03, -1.4648e-03,  6.1646e-03,  ...,  2.0599e-03,\n",
      "         -1.5717e-03, -4.2419e-03]], device='cuda:0', dtype=torch.float16)\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "CUDA kernel failed: an illegal memory access was encountered\nException raised from genEmbeddingLauncher at /code/pllm/compute-bound/python_binding/pybind/bind_genEmbedding.cu:63 (most recent call first):\nframe #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f06e096c446 in /root/anaconda3/lib/python3.11/site-packages/torch/lib/libc10.so)\nframe #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f06e09166e4 in /root/anaconda3/lib/python3.11/site-packages/torch/lib/libc10.so)\nframe #2: <unknown function> + 0x78c2 (0x7f06e0cdc8c2 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #3: <unknown function> + 0xbb30 (0x7f06e0ce0b30 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #4: <unknown function> + 0x13f76 (0x7f06e0ce8f76 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #5: /root/anaconda3/bin/python() [0x528187]\nframe #6: _PyObject_MakeTpCall + 0x27c (0x503a0c in /root/anaconda3/bin/python)\nframe #7: _PyEval_EvalFrameDefault + 0x6a3 (0x510f33 in /root/anaconda3/bin/python)\nframe #8: /root/anaconda3/bin/python() [0x5cb55a]\nframe #9: PyEval_EvalCode + 0x9f (0x5cac2f in /root/anaconda3/bin/python)\nframe #10: /root/anaconda3/bin/python() [0x5e43d3]\nframe #11: _PyEval_EvalFrameDefault + 0x3585 (0x513e15 in /root/anaconda3/bin/python)\nframe #12: /root/anaconda3/bin/python() [0x5dfdea]\nframe #13: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #14: /root/anaconda3/bin/python() [0x5dfdea]\nframe #15: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #16: /root/anaconda3/bin/python() [0x5dfdea]\nframe #17: /root/anaconda3/bin/python() [0x5e2446]\nframe #18: _PyEval_EvalFrameDefault + 0x3707 (0x513f97 in /root/anaconda3/bin/python)\nframe #19: /root/anaconda3/bin/python() [0x55771f]\nframe #20: /root/anaconda3/bin/python() [0x556f0e]\nframe #21: PyObject_Call + 0x12c (0x5426bc in /root/anaconda3/bin/python)\nframe #22: _PyEval_EvalFrameDefault + 0x4761 (0x514ff1 in /root/anaconda3/bin/python)\nframe #23: /root/anaconda3/bin/python() [0x5dfdea]\nframe #24: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #25: /root/anaconda3/bin/python() [0x5dfdea]\nframe #26: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #27: /root/anaconda3/bin/python() [0x5dfdea]\nframe #28: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #29: /root/anaconda3/bin/python() [0x5dfdea]\nframe #30: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #31: /root/anaconda3/bin/python() [0x5dfdea]\nframe #32: <unknown function> + 0x79e7 (0x7f0707b3d9e7 in /root/anaconda3/lib/python3.11/lib-dynload/_asyncio.cpython-311-x86_64-linux-gnu.so)\nframe #33: /root/anaconda3/bin/python() [0x52637b]\nframe #34: /root/anaconda3/bin/python() [0x4c6b2d]\nframe #35: /root/anaconda3/bin/python() [0x4cb9f8]\nframe #36: /root/anaconda3/bin/python() [0x51e1c7]\nframe #37: _PyEval_EvalFrameDefault + 0x9025 (0x5198b5 in /root/anaconda3/bin/python)\nframe #38: /root/anaconda3/bin/python() [0x5cb55a]\nframe #39: PyEval_EvalCode + 0x9f (0x5cac2f in /root/anaconda3/bin/python)\nframe #40: /root/anaconda3/bin/python() [0x5e43d3]\nframe #41: /root/anaconda3/bin/python() [0x51e1c7]\nframe #42: PyObject_Vectorcall + 0x31 (0x51e0b1 in /root/anaconda3/bin/python)\nframe #43: _PyEval_EvalFrameDefault + 0x6a3 (0x510f33 in /root/anaconda3/bin/python)\nframe #44: _PyFunction_Vectorcall + 0x173 (0x538733 in /root/anaconda3/bin/python)\nframe #45: /root/anaconda3/bin/python() [0x5f6a1f]\nframe #46: Py_RunMain + 0x14a (0x5f642a in /root/anaconda3/bin/python)\nframe #47: Py_BytesMain + 0x39 (0x5bb3d9 in /root/anaconda3/bin/python)\nframe #48: <unknown function> + 0x29d90 (0x7f0708c7ad90 in /lib/x86_64-linux-gnu/libc.so.6)\nframe #49: __libc_start_main + 0x80 (0x7f0708c7ae40 in /lib/x86_64-linux-gnu/libc.so.6)\nframe #50: /root/anaconda3/bin/python() [0x5bb223]\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 218\u001b[0m\n\u001b[1;32m    216\u001b[0m pipeline\u001b[38;5;241m.\u001b[39mconfig()\n\u001b[1;32m    217\u001b[0m pipeline\u001b[38;5;241m.\u001b[39mupdate(input_ids)\n\u001b[0;32m--> 218\u001b[0m pipeline\u001b[38;5;241m.\u001b[39mrun()\n",
      "Cell \u001b[0;32mIn[9], line 205\u001b[0m, in \u001b[0;36mPipeline.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28mprint\u001b[39m(executor\u001b[38;5;241m.\u001b[39mordered_operations)\n\u001b[1;32m    204\u001b[0m \u001b[38;5;66;03m# executor.execute({})\u001b[39;00m\n\u001b[0;32m--> 205\u001b[0m executor\u001b[38;5;241m.\u001b[39mprint_debug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mout.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[7], line 68\u001b[0m, in \u001b[0;36mExecutor.print_debug\u001b[0;34m(self, filename)\u001b[0m\n\u001b[1;32m     64\u001b[0m     torch\u001b[38;5;241m.\u001b[39msave(weights\u001b[38;5;241m.\u001b[39mweight_map[layer]\u001b[38;5;241m.\u001b[39mcpu(), \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m./out/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mop\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mweights\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     66\u001b[0m f\u001b[38;5;241m.\u001b[39mflush()\n\u001b[0;32m---> 68\u001b[0m op\u001b[38;5;241m.\u001b[39mrun(layer)  \u001b[38;5;66;03m# Execute the operation\u001b[39;00m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m outputs \u001b[38;5;129;01min\u001b[39;00m op\u001b[38;5;241m.\u001b[39moutputs\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m     71\u001b[0m     f\u001b[38;5;241m.\u001b[39mwrite(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mop\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlayer\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00moutputs\u001b[38;5;241m.\u001b[39mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[0;32mIn[4], line 221\u001b[0m, in \u001b[0;36mGenEmbedding.run\u001b[0;34m(self, layer)\u001b[0m\n\u001b[1;32m    219\u001b[0m \u001b[38;5;66;03m# create a new tensor to store the output\u001b[39;00m\n\u001b[1;32m    220\u001b[0m output_temp \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros((\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_size, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhidden_dim), dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat16, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtoken\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mtensor\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m--> 221\u001b[0m bind_genEmbedding\u001b[38;5;241m.\u001b[39mgenEmbedding(tokens, embedding, output_temp)\n\u001b[1;32m    223\u001b[0m \u001b[38;5;66;03m# self.outputs[\"output\"].tensor.copy_(embedding[tokens])\u001b[39;00m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moutput\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mtensor\u001b[38;5;241m.\u001b[39mcopy_(output_temp)\n",
      "\u001b[0;31mRuntimeError\u001b[0m: CUDA kernel failed: an illegal memory access was encountered\nException raised from genEmbeddingLauncher at /code/pllm/compute-bound/python_binding/pybind/bind_genEmbedding.cu:63 (most recent call first):\nframe #0: c10::Error::Error(c10::SourceLocation, std::string) + 0x96 (0x7f06e096c446 in /root/anaconda3/lib/python3.11/site-packages/torch/lib/libc10.so)\nframe #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::string const&) + 0x64 (0x7f06e09166e4 in /root/anaconda3/lib/python3.11/site-packages/torch/lib/libc10.so)\nframe #2: <unknown function> + 0x78c2 (0x7f06e0cdc8c2 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #3: <unknown function> + 0xbb30 (0x7f06e0ce0b30 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #4: <unknown function> + 0x13f76 (0x7f06e0ce8f76 in /code/pllm/compute-bound/python_binding/playground/../../build/bind_genEmbedding.cpython-311-x86_64-linux-gnu.so)\nframe #5: /root/anaconda3/bin/python() [0x528187]\nframe #6: _PyObject_MakeTpCall + 0x27c (0x503a0c in /root/anaconda3/bin/python)\nframe #7: _PyEval_EvalFrameDefault + 0x6a3 (0x510f33 in /root/anaconda3/bin/python)\nframe #8: /root/anaconda3/bin/python() [0x5cb55a]\nframe #9: PyEval_EvalCode + 0x9f (0x5cac2f in /root/anaconda3/bin/python)\nframe #10: /root/anaconda3/bin/python() [0x5e43d3]\nframe #11: _PyEval_EvalFrameDefault + 0x3585 (0x513e15 in /root/anaconda3/bin/python)\nframe #12: /root/anaconda3/bin/python() [0x5dfdea]\nframe #13: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #14: /root/anaconda3/bin/python() [0x5dfdea]\nframe #15: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #16: /root/anaconda3/bin/python() [0x5dfdea]\nframe #17: /root/anaconda3/bin/python() [0x5e2446]\nframe #18: _PyEval_EvalFrameDefault + 0x3707 (0x513f97 in /root/anaconda3/bin/python)\nframe #19: /root/anaconda3/bin/python() [0x55771f]\nframe #20: /root/anaconda3/bin/python() [0x556f0e]\nframe #21: PyObject_Call + 0x12c (0x5426bc in /root/anaconda3/bin/python)\nframe #22: _PyEval_EvalFrameDefault + 0x4761 (0x514ff1 in /root/anaconda3/bin/python)\nframe #23: /root/anaconda3/bin/python() [0x5dfdea]\nframe #24: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #25: /root/anaconda3/bin/python() [0x5dfdea]\nframe #26: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #27: /root/anaconda3/bin/python() [0x5dfdea]\nframe #28: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #29: /root/anaconda3/bin/python() [0x5dfdea]\nframe #30: _PyEval_EvalFrameDefault + 0x327e (0x513b0e in /root/anaconda3/bin/python)\nframe #31: /root/anaconda3/bin/python() [0x5dfdea]\nframe #32: <unknown function> + 0x79e7 (0x7f0707b3d9e7 in /root/anaconda3/lib/python3.11/lib-dynload/_asyncio.cpython-311-x86_64-linux-gnu.so)\nframe #33: /root/anaconda3/bin/python() [0x52637b]\nframe #34: /root/anaconda3/bin/python() [0x4c6b2d]\nframe #35: /root/anaconda3/bin/python() [0x4cb9f8]\nframe #36: /root/anaconda3/bin/python() [0x51e1c7]\nframe #37: _PyEval_EvalFrameDefault + 0x9025 (0x5198b5 in /root/anaconda3/bin/python)\nframe #38: /root/anaconda3/bin/python() [0x5cb55a]\nframe #39: PyEval_EvalCode + 0x9f (0x5cac2f in /root/anaconda3/bin/python)\nframe #40: /root/anaconda3/bin/python() [0x5e43d3]\nframe #41: /root/anaconda3/bin/python() [0x51e1c7]\nframe #42: PyObject_Vectorcall + 0x31 (0x51e0b1 in /root/anaconda3/bin/python)\nframe #43: _PyEval_EvalFrameDefault + 0x6a3 (0x510f33 in /root/anaconda3/bin/python)\nframe #44: _PyFunction_Vectorcall + 0x173 (0x538733 in /root/anaconda3/bin/python)\nframe #45: /root/anaconda3/bin/python() [0x5f6a1f]\nframe #46: Py_RunMain + 0x14a (0x5f642a in /root/anaconda3/bin/python)\nframe #47: Py_BytesMain + 0x39 (0x5bb3d9 in /root/anaconda3/bin/python)\nframe #48: <unknown function> + 0x29d90 (0x7f0708c7ad90 in /lib/x86_64-linux-gnu/libc.so.6)\nframe #49: __libc_start_main + 0x80 (0x7f0708c7ae40 in /lib/x86_64-linux-gnu/libc.so.6)\nframe #50: /root/anaconda3/bin/python() [0x5bb223]\n"
     ]
    }
   ],
   "source": [
    "import transformers\n",
    "import os\n",
    "os.environ[\"HF_HOME\"] = \"/code/hf\"\n",
    "\n",
    "class Pipeline():\n",
    "    def __init__(self):\n",
    "        # Set parameters as instance variables.\n",
    "        self.num_kv_heads = 8\n",
    "        self.num_qo_heads = 32\n",
    "        self.kqv_heads = self.num_qo_heads + 2 * self.num_kv_heads\n",
    "        self.head_dim = 128\n",
    "        self.vocab_size = 128256\n",
    "        self.hidden_dim = 4096\n",
    "        self.intermediate_dim = 14 * 1024\n",
    "        self.batch_size = 7\n",
    "        self.layer = 3\n",
    "\n",
    "    def init(self, weight_path):\n",
    "        self.init_external_data()\n",
    "        self.init_operations()\n",
    "        self.init_dependency()\n",
    "        self.init_set_shape()\n",
    "        self.init_set_weight(weight_path)\n",
    "\n",
    "    def init_external_data(self):\n",
    "        self.kv_cache = KVCacheNone()\n",
    "\n",
    "    def init_operations(self):\n",
    "        self.global_input    = GlobalInput(\"GlobalInput\").first_only()\n",
    "\n",
    "        self.gen_embedding   = GenEmbedding(\"GenEmbedding\").setWeightName(\"model.embed_tokens.weight\").first_only()\n",
    "\n",
    "        self.layerNormAttn   = LayerNorm(\"LayerNormAttn\").setWeightName(\"model.layers.{layer}.input_layernorm.weight\")\n",
    "\n",
    "        self.kqv             = GEMMCombineWeight(\"KQV\").setWeightName([\n",
    "            \"model.layers.{layer}.self_attn.q_proj.weight\",\n",
    "            \"model.layers.{layer}.self_attn.k_proj.weight\",\n",
    "            \"model.layers.{layer}.self_attn.v_proj.weight\"\n",
    "        ])\n",
    "\n",
    "        self.ropeAppend      = RopeAppend(\"RopeAppend\")\n",
    "        self.ropeAppend.externals[\"KVCache\"] = self.kv_cache\n",
    "\n",
    "        self.decAttn         = DecAttn(\"DecAttn\")\n",
    "        self.decAttn.externals[\"KVCache\"] = self.kv_cache\n",
    "\n",
    "        self.pfAttn          = PFAttn(\"PFAttn\")\n",
    "        self.pfAttn.externals[\"KVCache\"] = self.kv_cache\n",
    "\n",
    "        self.o               = GEMM(\"O\").setWeightName(\"model.layers.{layer}.self_attn.o_proj.weight\")\n",
    "\n",
    "        self.layerNormFFN    = LayerNorm(\"LayerNormFFN\").setWeightName(\"model.layers.{layer}.post_attention_layernorm.weight\")\n",
    "\n",
    "        self.ug              = GEMMCombineWeight(\"UG\").setWeightName([\n",
    "            \"model.layers.{layer}.mlp.up_proj.weight\",\n",
    "            \"model.layers.{layer}.mlp.gate_proj.weight\"\n",
    "        ])\n",
    "\n",
    "        self.activation      = Activation(\"Activation\")\n",
    "\n",
    "        self.d               = GEMM(\"D\").setWeightName(\"model.layers.{layer}.mlp.down_proj.weight\")\n",
    "\n",
    "        self.getLogits       = GEMMNoBias(\"GetLogits\").setWeightName(\"lm_head.weight\")\n",
    "        self.getLogits.last_layer_only = True\n",
    "\n",
    "        self.modelLayerNorm  = LayerNorm(\"ModelLayerNorm\").setWeightName(\"model.norm.weight\")\n",
    "        self.modelLayerNorm.last_layer_only = True\n",
    "\n",
    "        self.sample          = Sampling(\"Sampling\")\n",
    "        self.sample.last_layer_only = True\n",
    "\n",
    "        self.global_output   = GlobalOutput(\"GlobalOutput\")\n",
    "        self.global_output.last_layer_only = True\n",
    "\n",
    "        # Save operations in an instance variable.\n",
    "        self.operation_list = [\n",
    "            self.global_input, self.gen_embedding, self.layerNormAttn, self.kqv, self.ropeAppend,\n",
    "            self.decAttn, self.pfAttn, self.o, self.layerNormFFN, self.ug, self.activation, self.d,\n",
    "            self.modelLayerNorm, self.getLogits, self.sample, self.global_output\n",
    "        ]\n",
    "    \n",
    "    def init_dependency(self):\n",
    "        self.global_input.outputs[\"tokens\"] >> self.gen_embedding.inputs[\"token\"]\n",
    "\n",
    "        self.gen_embedding.outputs[\"output\"] >> self.layerNormAttn.inputs[\"input\"]\n",
    "\n",
    "        self.layerNormAttn.outputs[\"output\"] >> self.kqv.inputs[\"A\"]\n",
    "\n",
    "        self.kqv.outputs[\"D\"] >> self.ropeAppend.inputs[\"kqv\"]\n",
    "\n",
    "        self.ropeAppend.outputs[\"q\"] >> self.decAttn.inputs[\"Q\"]\n",
    "        self.ropeAppend.outputs[\"q\"] >> self.pfAttn.inputs[\"Q\"]\n",
    "\n",
    "        self.decAttn.outputs[\"output\"] >> self.o.inputs[\"A\"]\n",
    "        self.pfAttn.outputs[\"output\"] >> self.o.inputs[\"A\"]\n",
    "        self.gen_embedding.outputs[\"output\"] >> self.o.inputs[\"C\"]\n",
    "\n",
    "        self.o.outputs[\"D\"] >> self.layerNormFFN.inputs[\"input\"]\n",
    "\n",
    "        self.layerNormFFN.outputs[\"output\"] >> self.ug.inputs[\"A\"]\n",
    "\n",
    "        self.ug.outputs[\"D\"] >> self.activation.inputs[\"input\"]\n",
    "\n",
    "        self.activation.outputs[\"output\"] >> self.d.inputs[\"A\"]\n",
    "\n",
    "        self.o.outputs[\"D\"] >> self.d.inputs[\"C\"]\n",
    "        # Additional dependency: d feeds back to layerNormAttn and o.inputs[\"C\"]\n",
    "        self.d.outputs[\"D\"].chain(self.layerNormAttn.inputs[\"input\"], True)\n",
    "        self.d.outputs[\"D\"].chain(self.o.inputs[\"C\"], True)\n",
    "        self.d.outputs[\"D\"] >> self.modelLayerNorm.inputs[\"input\"]\n",
    "\n",
    "        self.modelLayerNorm.outputs[\"output\"] >> self.getLogits.inputs[\"A\"]\n",
    "\n",
    "        self.getLogits.outputs[\"D\"] >> self.sample.inputs[\"logits\"]\n",
    "\n",
    "        self.sample.outputs[\"tokens\"] >> self.global_output.inputs[\"tokens\"]\n",
    "        \n",
    "        for operation in self.operation_list:\n",
    "            operation.checkConnection()\n",
    "    \n",
    "    def init_set_shape(self):\n",
    "        self.gen_embedding.setShape(self.hidden_dim, self.vocab_size)\n",
    "        self.layerNormAttn.setShape(self.hidden_dim)\n",
    "        self.kqv.setShape(self.kqv_heads * self.head_dim, self.hidden_dim)\n",
    "        self.decAttn.setShape(self.num_kv_heads, self.num_qo_heads, self.head_dim)\n",
    "        self.pfAttn.setShape(self.num_kv_heads, self.num_qo_heads, self.head_dim)\n",
    "        self.ropeAppend.setShape(self.num_kv_heads, self.num_qo_heads, self.head_dim)\n",
    "        self.o.setShape(self.hidden_dim, self.hidden_dim)\n",
    "        self.layerNormFFN.setShape(self.hidden_dim)\n",
    "        self.ug.setShape(self.intermediate_dim * 2, self.hidden_dim)\n",
    "        self.d.setShape(self.hidden_dim, self.intermediate_dim)\n",
    "        self.activation.setShape(self.intermediate_dim)\n",
    "        self.modelLayerNorm.setShape(self.hidden_dim)\n",
    "        self.getLogits.setShape(self.vocab_size, self.hidden_dim)\n",
    "        self.sample.setShape(self.vocab_size)\n",
    "    \n",
    "    def init_set_weight(self, weight_path):\n",
    "        weight_manager = WeightManager()\n",
    "        weight_manager.load_from_safe_tensor(weight_path)\n",
    "        weight_manager.set_weight(self.operation_list, self.layer)\n",
    "    \n",
    "    def config_batch_size(self):\n",
    "        self.gen_embedding.setBatchSize(self.batch_size)\n",
    "        self.layerNormAttn.setBatchSize(self.batch_size)\n",
    "        self.kqv.setBatchSize(self.batch_size)\n",
    "        self.decAttn.setBatchSize(0)\n",
    "        self.pfAttn.setBatchSize(self.batch_size)\n",
    "        self.ropeAppend.setBatchSize(self.batch_size)\n",
    "        self.layerNormFFN.setBatchSize(self.batch_size)\n",
    "        self.ug.setBatchSize(self.batch_size)\n",
    "        self.activation.setBatchSize(self.batch_size)\n",
    "        self.o.setBatchSize(self.batch_size)\n",
    "        self.d.setBatchSize(self.batch_size)\n",
    "        self.modelLayerNorm.setBatchSize(self.batch_size)\n",
    "        self.getLogits.setBatchSize(self.batch_size)\n",
    "        self.sample.setBatchSize(self.batch_size)\n",
    "        self.global_input.setBatchSize(self.batch_size)\n",
    "        self.global_output.setBatchSize(self.batch_size)\n",
    "    \n",
    "    def config_algorithm(self):\n",
    "        pass\n",
    "\n",
    "    def config(self):\n",
    "        self.config_batch_size()\n",
    "        self.config_algorithm()\n",
    "    \n",
    "    def update(self, input_ids):\n",
    "        \n",
    "        self.update_allocate_buffers()\n",
    "        # concatenate input_ids into a single tensor\n",
    "        flattened = [item for sublist in input_ids for item in sublist]\n",
    "        input_tensor = torch.tensor(flattened, dtype=torch.int32, device='cuda')\n",
    "        # get cumulative sum of the number of tokens in each input\n",
    "        request_length = torch.tensor([len(x) for x in input_ids], dtype=torch.int32)\n",
    "        cumsum_input = torch.cat([torch.tensor([0], dtype=torch.int32), torch.cumsum(request_length, dim=0)])\n",
    "        print(f\"cumsum_input: {cumsum_input}\")\n",
    "        print(f\"input_tensor: {input_tensor}\")\n",
    "        \n",
    "        self.global_input.outputs[\"tokens\"].tensor[:input_tensor.shape[0]].copy_(input_tensor)\n",
    "        self.ropeAppend.update(cumsum_input)\n",
    "        self.decAttn.update(cumsum_input)\n",
    "        self.pfAttn.update(cumsum_input)\n",
    "        \n",
    "\n",
    "    \n",
    "    def update_allocate_buffers(self):\n",
    "        # Build list of buffers.\n",
    "        buffers_list = []\n",
    "        for operation in self.operation_list:\n",
    "            for _, wrapper in operation.inputs.items():\n",
    "                buffers_list.append(wrapper)\n",
    "            for _, wrapper in operation.outputs.items():\n",
    "                buffers_list.append(wrapper)\n",
    "        # Allocate buffers.\n",
    "        bufferAllocator = BufferAllocator(buffers_list)\n",
    "        bufferAllocator.allocate_buffer(plot=True)\n",
    "        print(f\"Total allocated: {bufferAllocator.total_allocated / 1024 / 1024} MB\")\n",
    "\n",
    "    def run(self):\n",
    "        executor = Executor(self.operation_list, self.layer)\n",
    "        executor.plan_layer_ordering()\n",
    "        executor.draw_ordered_graph()\n",
    "        print(executor.ordered_operations)\n",
    "        # executor.execute({})\n",
    "        executor.print_debug(\"out.txt\")\n",
    "\n",
    "\n",
    "from transformers import AutoTokenizer\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"meta-llama/Meta-Llama-3-8B-Instruct\")\n",
    "input_strings = [\"Hi, who are you?\"]\n",
    "input_ids = [tokenizer.encode(s) for s in input_strings]\n",
    "print(input_ids)\n",
    "\n",
    "pipeline = Pipeline()\n",
    "pipeline.init(\"/code/hf/hub/models--meta-llama--Meta-Llama-3-8B-Instruct/snapshots/5f0b02c75b57c5855da9ae460ce51323ea669d8a\")\n",
    "pipeline.config()\n",
    "pipeline.update(input_ids)\n",
    "pipeline.run()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NPartitionDistributeGEMM(Operations):\n",
    "    def __init__(self, name, nranks):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"A\": IOWrapper(self, 'A', IOBufferType.FULL),\n",
    "            \"C\": IOWrapper(self, 'C', IOBufferType.DiscontinousPartition)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"D\": IOWrapper(self, 'D', IOBufferType.DiscontinousPartition)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"B\": WeightWrapper()\n",
    "        }\n",
    "        self.nranks = nranks\n",
    "    \n",
    "    # here refering to the whole tensor shape\n",
    "    def setShape(self, N, K):\n",
    "        self.N = N // self.nranks\n",
    "        if N % self.nranks != 0:\n",
    "            raise ValueError(f\"Number of ranks {self.nranks} does not divide N={N}\")\n",
    "        self.K = K\n",
    "        self.weights[\"B\"].shape = (self.K, self.N)\n",
    "        \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"A\"].shape = (self.M, self.K)\n",
    "        self.inputs[\"C\"].shape = (self.M, self.N)\n",
    "        self.outputs[\"D\"].shape = (self.M, self.N)\n",
    "        \n",
    "    def run(self, layer, rank):\n",
    "        A = self.inputs[\"A\"].tensor\n",
    "        C = self.inputs[\"C\"].tensor\n",
    "        B = self.weights[\"B\"].weight_map[layer][rank]\n",
    "        self.outputs[\"D\"].tensor.copy_(A.matmul(B) + C)\n",
    "        \n",
    "    def setWeightName(self, name_list):\n",
    "        # is name list is just a string, then convert it to a list\n",
    "        if isinstance(name_list, str):\n",
    "            name_list = [name_list]\n",
    "        self.weight_name = name_list\n",
    "        return self\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached=False):\n",
    "        self.weights[\"B\"].weight_map = {}\n",
    "        for l in range(total_layers):\n",
    "            all_weight = torch.cat([global_weight_map[name.format(layer=l)].t() for name in self.weight_name], dim=1)\n",
    "            chunked  = all_weight.chunk(self.nranks, dim=1)\n",
    "            for t in chunked:\n",
    "                t = t.contiguous()\n",
    "            self.weights[\"B\"].weight_map[l] = all_weight.chunk(self.nranks, dim=1)\n",
    "        return self.weights[\"B\"]\n",
    "    \n",
    "class KPartitionDistributeGEMM(Operations):\n",
    "    def __init__(self, name, nranks):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"A\": IOWrapper(self, 'A', IOBufferType.DiscontinousPartition),\n",
    "            \"C\": IOWrapper(self, 'C', IOBufferType.FULL)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"D\": IOWrapper(self, 'D', IOBufferType.DiscontinousPartition)\n",
    "        }\n",
    "        self.weights = {\n",
    "            \"B\": WeightWrapper()\n",
    "        }\n",
    "        self.nranks = nranks\n",
    "    \n",
    "    def setShape(self, N, K):\n",
    "        self.N = N\n",
    "        self.K = K // self.nranks\n",
    "        if K % self.nranks != 0:\n",
    "            raise ValueError(f\"Number of ranks {self.nranks} does not divide K={K}\")\n",
    "        self.weights[\"B\"].shape = (self.K, self.N)\n",
    "    \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"A\"].shape = (self.M, self.K)\n",
    "        self.inputs[\"C\"].shape = (self.M, self.N)\n",
    "        self.outputs[\"D\"].shape = (self.M, self.N)\n",
    "    \n",
    "    def run(self, layer, rank):\n",
    "        A = self.inputs[\"A\"].tensor\n",
    "        C = self.inputs[\"C\"].tensor\n",
    "        B = self.weights[\"B\"].weight_map[layer][rank]\n",
    "        self.outputs[\"D\"].tensor.copy_(A.matmul(B) + C)\n",
    "    \n",
    "    def setWeightName(self, name_list):\n",
    "        # is name list is just a string, then convert it to a list\n",
    "        if isinstance(name_list, str):\n",
    "            name_list = [name_list]\n",
    "        self.weight_name = name_list\n",
    "        return self\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached=False):\n",
    "        self.weights[\"B\"].weight_map = {}\n",
    "        for l in range(total_layers):\n",
    "            all_weight = torch.cat([global_weight_map[name.format(layer=l)] for name in self.weight_name], dim=0)\n",
    "            chunked  = all_weight.chunk(self.nranks, dim=0)\n",
    "            for t in chunked:\n",
    "                t = t.contiguous()\n",
    "            self.weights[\"B\"].weight_map[l] = all_weight.chunk(self.nranks, dim=0)\n",
    "        return self.weights[\"B\"]\n",
    "    \n",
    "class AllGather(Operations):\n",
    "    def __init__(self, name, nranks):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"input\": IOWrapper(self, 'input', IOBufferType.DiscontinousPartition)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.FULL)\n",
    "        }\n",
    "        self.nranks = nranks\n",
    "    \n",
    "    def setShape(self, N):\n",
    "        self.N = N\n",
    "        self.inputs[\"input\"].shape = (self.N // self.nranks, self.nranks)\n",
    "        self.outputs[\"output\"].shape = (self.N,)\n",
    "    \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"input\"].shape = (self.M, self.N // self.nranks)\n",
    "        self.outputs[\"output\"].shape = (self.M, self.N)\n",
    "    \n",
    "    def run(self, layer):\n",
    "        self.outputs[\"output\"].tensor.copy_(self.inputs[\"input\"].tensor.repeat(1, self.nranks))\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached=False):\n",
    "        pass\n",
    "\n",
    "class AllReduce(Operations):\n",
    "    def __init__(self, name, nranks):\n",
    "        super().__init__(name)\n",
    "        self.inputs = {\n",
    "            \"input\": IOWrapper(self, 'input', IOBufferType.FULL)\n",
    "        }\n",
    "        self.outputs = {\n",
    "            \"output\": IOWrapper(self, 'output', IOBufferType.FULL)\n",
    "        }\n",
    "        self.nranks = nranks\n",
    "    \n",
    "    def setShape(self, N):\n",
    "        self.N = N\n",
    "        self.inputs[\"input\"].shape = (self.N,)\n",
    "        self.outputs[\"output\"].shape = (self.N,)\n",
    "    \n",
    "    def setBatchSize(self, M):\n",
    "        self.M = M\n",
    "        self.inputs[\"input\"].shape = (self.M, self.N)\n",
    "        self.outputs[\"output\"].shape = (self.M, self.N)\n",
    "    \n",
    "    def run(self, layer):\n",
    "        self.outputs[\"output\"].tensor.copy_(self.inputs[\"input\"].tensor.sum(dim=0) / self.nranks)\n",
    "    \n",
    "    def processWeight(self, global_weight_map, total_layers, cached=False):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import LlamaForCausalLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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